Scalable Extraction of Training Data from (Production) Language Models (2024)

Milad Nasr ${}^{*1}$ Nicholas Carlini ${}^{*1}$ Jonathan Hayase ${}^{1,2}$ Matthew Jagielski ${}^{1}$
A. Feder Cooper ${}^{3}$ Daphne Ippolito ${}^{1,4}$ Christopher A. Choquette-Choo ${}^{1}$
Eric Wallace ${}^{5}$ Florian Tramèr ${}^{6}$ Katherine Lee ${}^{+1,3}$
${}^{1}$ Google DeepMind ${}^{2}$ University of Washington ${}^{3}$ Cornell ${}^{4}$ CMU ${}^{5}$ UC Berkeley ${}^{6}$ ETH Zurich
${}^{*}$ Equal contribution ${}^{+}$ Senior author

Abstract

This paper studies extractable memorization:training data that an adversary can efficiently extractby querying a machine learning model without prior knowledge of the training dataset.We show an adversary can extract gigabytes of training data fromopen-source language models like Pythia or GPT-Neo,semi-open models like LLaMA or Falcon,and closed models like ChatGPT.Existing techniques from the literature suffice to attackunaligned models; in order to attack the aligned ChatGPT, we develop a newdivergence attack that causes the model to diverge from its chatbot-style generations and emit training data at a rate $150\times$ higher than when behaving properly.Our methods show practical attacks can recover far moredata than previously thought, andreveal that current alignment techniquesdo not eliminate memorization.

1 Introduction

Large language models (LLMs) memorize examples from their training datasets,which can allow an attacker to extract(potentially private) information [12, 7, 14].Prior work has(a) performed large-scale studies of the total quantity of memorized training data for open-source models [11], and(b) developed practical attacks to extract training data on (relatively) small models like GPT-2, by manually annotating examples as memorized or not [14].

In this paper, we unify these two directions andperform a large-scale study of“extractable memorization” in language models.Unlike discoverable memorization [11] that captures an upper bound onall training data that is memorized (even if it can only be recoveredby prompting the model with other training data),extractable memorization captures only that data that can be efficientlyrecovered by an adversary.We develop a scalable methodology that allows us to detect memorization intrillions of tokens of model outputs in terabyte-sized datasets,and perform this analysis on bothopen-source models (e.g., Pythia [5], GPT-Neo [6]) andsemi-open models (e.g., LLaMA [49], Falcon [40]).We find that larger and more capable models are more vulnerable to dataextraction attacks.

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But when we perform this analysis on gpt-3.5-turbo,it appears to memorize almost no training data.We hypothesize that this is because ChatGPT has been aligned (with RLHF [44, 39, 37, 35]) to act as a helpful chat assistant.¹¹1While limited information is available about this model,similar models like GPT-4 have been trained to “refuse to answer certain types of requests,” including those related to training data extraction [37, p. 13].

To circumvent the model’s alignment,we discover a prompting strategy that causesgpt-3.5-turbo to “diverge” from reasonable, chatbot-style generations, and tobehave like a base language model, outputting text in a typical Internet-text style.In order to check whether this emitted text was previously contained somewhere on the Internet, we merge together several publicly available web-scale training sets into a nine terabyte dataset.By matching against this dataset, we recover over ten thousand examples from ChatGPT’s training dataset at a query cost of $200 USD—and our scaling estimate suggests that one could extract over $10\times$ more data with more queries.

Ethics & Responsible Disclosure.

We have taken great care to responsibly share our findings.We shared our findings with the authors of each model we study in this paper (e.g., OPT [54], Falcon [40], Mistral [28], and LLaMA [49]),.

Our attack on ChatGPT (gpt-3.5-turbo) is specific to this model and, to the bestof our knowledge, is not applicable to any other production language model that we have tested.We disclosed this vulnerability to OpenAI on August 30th (after discovering the flaw on July 11th),and allowed 90 days for the issue to be addressedfollowing standard disclosure timelines [41] before publishing this paper.

We believe it is now safe to share this finding, and that publishing it openly brings necessary, greater attention to the data security and alignment challenges of generative AI models.²²2In fact,in early August, a month after we initial discoveredthis attack, multiple independent researchers discovered the underlying exploit used in ourpaper, but, like us initially, they did not realize that the model wasregenerating training data, e.g., https://twitter.com/nostalgebraist/status/1686576041803096065.Our paper helps to warn practitioners that they should not train and deploy LLMs for any privacy-sensitive applications without extreme safeguards.

2 Background and Related Work

Training data for language models.

State-of-the-art large language models (LLMs) are pre-trained on vast text corpora that consist of billions to trillions of tokens [42, 6, 43, 50]. For proprietary models such as GPT-4 [38] and PaLM 2 [2], these training sets are kept secret to presumably hide (1) the company’s proprietary data collection pipeline, and (2) any private, user-specific, or licensed training data that is not publicly available [32, 31].

Instruction-tuning and RLHF.

Pre-trained LLMs can solve numerous downstream tasks by conditioning on natural language instructions [8]. The model’s utility can be drastically improved via supervised fine-tuning or RLHF on instruction-following data [44, 39, 3, 18, 38, 36]. Aside from utility, this “alignment” stage can also train models to use a unified chat-like persona [39, 35] and to abstain from answering on certain types of queries (e.g., it will not assist users in writing spam emails) [37]. In this work, we analyze ChatGPT (specifically, the gpt-3.5-turbo model endpoint).

Privacy attacks.

Neural networks, especially ones with many parameters, can memorize their training data.This can be exploited by adversaries via membership inference attacks that infer whether an example was in the training set [45, 21, 52, 9, 17], as well as more powerful data extraction attacks [12, 14, 4, 30] that recover full training examples. In this work, we conduct both types of attacks on LLMs.

3 Extracting Data from Open Models

We begin by studying data extraction attacks on open models where both the models’ parameters and their original training sets are publicly available.Thislets us precisely evaluate the performance ofextraction attacks from prior work.

3.1 Prior Approaches and Definitions

We follow the (conservative) definition of memorization of Carlini et al. (2021) [14]: given a model trained on a training set ${\mathbb{X}}$ , we denote a string ${\bm{x}}\in{\mathbb{X}}$ asmemorized if we can prompt the model’s generation routine $\mathsf{Gen}$ to produce the string ${\bm{x}}$ verbatim.Some prior work (e.g., [11, 47, 10]) has proposed more general notions of memorization where the model may generate a “close” copy of a training sample, but we restrict ourselves to verbatim matches as this will make it possible to scale our analysis to large datasets. This leads us to our definition of extractable memorization:³³3Prior work also uses the word “extractable” [14]; we supply a general definition that encompasses attacks in this work and our own.

Definition 1 (Extractable memorization).

Given a model with a generation routine $\mathsf{Gen}$ , an example ${\bm{x}}$ from the training set ${\mathbb{X}}$ is extractably memorized if an adversary (without access to ${\mathbb{X}}$ ) can construct a prompt ${\bm{p}}$ that makes the model produce ${\bm{x}}$ (i.e., $\mathsf{Gen}({\bm{p}})={\bm{x}}$ ).

The design and evaluation of extraction attacks in prior work were primarily hindered by two challenges:

1.
How should we design prompts that best elicit memorization in a model?
2.
How do we test whether the attack worked, i.e., whether the model’s output is training data or not?

Prior work has tackled these challenges with various heuristics. For example, Carlini et al. (2021) [14] recover training examples from the GPT-2 language model [42] by prompting itwith short strings sampled from the public Internet, and then manually checking whether these strings can also be found with a Google search.That is, they address the first challenge by simply prompting the model with data sampled from the model’s training distribution (GPT-2 was trained on some unknown text sampled from the Internet), and they address the second challenge by (reasonably) assuming that any string memorized by the model is also contained in Google’s search index; they manually query with output strings to see if they exist on the public Internet.

Their attack, while successful, only verifiably recovers $\approx 0.00001\%$ of GPT-2’s training dataset.The authors acknowledge that this is likely a loose lower bound; they could not produce a tighter estimate due to the time-consuming manual verification procedure that their attack involves.

Rather than improving this loose lower bound, subsequent work has instead focused on measuring an upper bound on the strength of an extraction attack, thereby circumventing the two challenges described above.Several works [11, 27] have studied the extent to which models can regurgitate their training data when explicitly prompted with data from their training set. That is, given a training string $[{\bm{p}}||{\bm{x}}]\in{\mathbb{X}}$ that consists of a prefix ${\bm{p}}$ and suffix ${\bm{x}}$ , we can measure whether the model can generate ${\bm{x}}$ when prompted with the true prefix ${\bm{p}}$ .Following Carlini et al. (2022) [11], we denote this as discoverable memorization:

Definition 2 (Discoverable memorization).

For a model $\mathsf{Gen}$ and an example $[{\bm{p}}||{\bm{x}}]$ from the training set ${\mathbb{X}}$ , we say that ${\bm{x}}$ is discoverably memorized if $\mathsf{Gen}({\bm{p}})={\bm{x}}$ .

Prior work shows that many LLMs discoverably memorize roughly $1\%$ of their training datasets (when prompting the model with about 50 tokens of context) [11, 2, 30].There is thus a huge gap between prior lower bounds on extractable memorization (i.e., actual extraction attacks that have to be manually verified [14]), and upper bounds that assume full access to the training set ${\mathbb{X}}$ .This raises a natural question: why is there such a large observed gap between extractable and discoverable memorization in the literature?

To answer this question, recall the differences between how prior work measured extractable and discoverable memorization rates:first, prompts are constructed by either heuristic means or by using the actual true prefix ${\bm{p}}$ ,and second, verifying if data was successfully extracted was either performed manually or by looking at the actual training dataset ${\mathbb{X}}$ .This suggests two possible explanations for the observed gap:

1.
It is possible that prompting models with training data leads to orders-of-magnitude more training-data regurgitation, compared to realistic extraction attack strategies (in which adversaries do not have access to the training set).
2.
Alternatively, perhaps existing extraction attacks already make models regurgitate large amounts of training data, but prior work was not able to verify that the model outputs were training data.

Our goal in this section is to disentangle these two possible explanations. As we will show, the latter explanation is (mostly) the correct one.Existing extraction attacks are actually a lot more successful at recovering training data than what prior work indicates.

3.2 Attack Methodology

To begin,we evaluatepast extraction attacks in a controlled setting where testing for attack success is possible.That is, we first focus on open-source models with publicly available training datasets, where we can mechanistically verify if any generated output ${\bm{x}}$ is indeed training data (but, crucially, the attack itself does not rely on knowledge of the training set).

We follow thedata extraction attack method of Carlini et al. [14]:(1) we download $10^{8}$ bytes of data from Wikipedia, and generate prompts ${\bm{p}}$ by randomly sampling (with replacement) hundreds of millions of continuous 5-token blocks from this dataset;(2) we perform an independent generation for each prompt ${\bm{p}}^{i}$ as $\mathsf{Gen}({\bm{p}}^{i})={\bm{x}}^{i}$ and store each ${\bm{x}}^{i}$ .

Our methodology differs in how we evaluate the efficacy of the attack.Because this prior attack extracted training data from a language modelwithout a public dataset, it was necessary to manually search the Internetin order to determine whether or not any generated sequence wascontained in the model’s training dataset.In contrast, each model we study in this section is fully open-source.This lets us directly query the model’s training data toevaluate whether or not any generatedsample is memorized.

Performing the training set inclusion test ${\bm{x}}\in{\mathbb{X}}$ naively is prohibitively expensive, as LLMs are trained on datasets with trillions of tokensand we generate billions of tokens of output from each of these models.To make this search efficient, we use a suffix array, as done in Lee et al. (2021) [33]—a data structure that stores all suffixes of the dataset in sorted order, and which enables fast string lookups (using binary search).We build a suffix array ${\bm{s}}$ over ${\mathbb{X}}$ , denoted ${\bm{s}}({\mathbb{X}})$ or simply ${\bm{s}}$ when unambiguous. We can then check that ${\bm{x}}\in{\bm{s}}$ , which is equivalent to checking ${\bm{x}}\in{\mathbb{X}}$ (see Appendix A).

We report that an extraction is successful if the model outputs text that contains a substring of length at least 50 tokens that is contained verbatim in the training set.⁴⁴4We also require that the entropy of the generated string is high, to filter out degenerate examples such as repeated whitespace, or lists of numbers.We chose this value empirically to be sufficiently large so that no two suffixes could accidentally overlap.We estimated the amount of token overlap between news articles guaranteed to be written after the creation of the largest training datasets RedPajama [19].We found no overlap longer than 25 tokens, excluding direct quotations(i.e., actual copies). We then chose to be extremely conservative and double this value.

3.3 Empirical Results

We apply our attack to 9 open-source models of different sizes.Since these models were, e.g., “designed specifically to facilitate scientificresearch” [5],they make available their entire training and pipeline and dataset,facilitying our study.

•
GPT-Neo (1.3B, 2.7B, 6B) [6], a family of models trained on The Pile [23].⁵⁵5The 6B paramter model is officially called GPT-J;for consistency and simplicity we refer to it as GPT-Neo 6B in this paper.
•
Pythia (1.4B, 1.4B-dedup, 6.9B, 6.9B-dedup) [5],a family of models also trained on The Pile, but primarily designed for studying model scaling and memorization.
•
RedPajama-INCITE (Base-3B-v1, Base-7B) [20],models trained on the RedPajama [19] dataset.

We generate one billion tokens of output for each model and then compute the number of memorized examplesby matching against the corresponding training set.From this data, we can perform two different types of analysis.First, in Table 1,we measure the fraction of model outputs that are memorized.We observe rates between 0.1% and 1%.But this number is hard to interpret—a model that emitted the samememorized training sequence thousands of times in a row would lookhighly non-private, even if in practice it was revealing almost nodata.

And so instead, we can also compute the number of unique 50-token strings that we extract,which varies between several hundred thousand and several million.This allows us to observe data extraction rates orders of magnitudehigher than reported previously in Carlini et al. (2021) [14, p. 13], which only verifiablyextracted 600 sequences from GPT-2.This serves as evidence to suggest thatextractable memorization rates are much higher thanpreviously thought (at least for these open models).We observe a strong correlation between model size andboth the rate of emitting memorized outputand also the total number of unique 50-token sequences we extract,indicating that the pathological failure mode wherea model repeatedly emits the same memorized example is not common in

Model	Parameters	% Tokens	Unique	Extrapolated
Family	(billions)	memorized	50-grams	50-grams
RedPajama	3	0.772%	1,596,928	7,234,680
RedPajama	7	1.438%	2,899,995	11,329,930
GPT-Neo	1.3	0.160%	365,479	2,107,541
GPT-Neo	2.7	0.236%	444,948	2,603,064
GPT-Neo	6	0.220%	591,475	3,564,957
Pythia	1.4	0.453%	811,384	4,366,732
Pythia-dedup	1.4	0.578%	837,582	4,147,688
Pythia	6.9	0.548%	1,281,172	6,762,021
Pythia-dedup	6.9	0.596%	1,313,758	6,761,831

3.4 Estimating Total Memorization

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In our explorations thus far (Sections 3.3 and 3.5), we have used a large fixed budget of generations for our extraction attacks. But, the number of generations has a significant impact on the amount of extractable memorization, as can be clearly seen from Figure 2: memorization grows (nearly) linearly even after generating several hundred billion tokens.

This leads to a natural question that has not yet been discussed in the literature: if we could query a model infinitely, how much memorization could we extract in total? Given this is infeasible, we instead aim to estimate the total memorization. However, again observing Figure 2 demonstrates a challenge here: the rate of extracting memorized training data is not a good predictor of the total quantity of memorization. In particular, we observe that at smaller compute budgets, Pythia 1.4B appears to memorize more data than the (larger) GPT-Neo 6B. However, if we query the model more, the rate of extractable memorization in Pythia-1.4B decreases, revealing that GPT-Neo 6B in fact memorizes more data in total. Thus, we will need to find better predictors of the total memorization of a model.

Extrapolating total memorization.

We begin by decomposingour extrapolation problem into estimating two values: 1) how often a model outputs anything memorized, and 2) how often a memorized generation is new.The first value is not stateful and so can be easily estimated as a probability. But, the second value depends on how many memorized strings we have already observed. Let us focus on this latter quantity. Note that the total amount of memorization the model will ever output as we scale the number of generations, does not depend on the first value.

We can visualize the rate of new memorization via a slight modification of Figure 2. Instead of varying the number of generated tokens, we instead compute and vary the number of memorized tokens extracted.In this visualization, shown in Figure 3, we can more clearly observe the differences between GPT-Neo 6B and Pythia 1.4B. In particular,the slope and curvature of the plot help us understand the model’s total memorization: Pythia-1.4 outputs new memorized examples less frequently than GPT-Neo 6B, and seems to saturate much more quickly as well, pointing to the limit of how much training data we can surface.While the slope and curvature are only estimations, they can serve as a starting point to understand how to make extractable memorization more efficient. Indeed, they can enable us to estimate how much memorization could be extracted even if researchers do not have the capability to generate many hundreds of billions of tokens.

Intuition.

Suppose a researcher wants to know how many fish live in a lake.If this researcher is very hardworking, they could try to count each fish individually, catching and then throwing them back in the lake, and hoping to not skip or double-count any fish.However, in practice, a common technique is known as mark-and-recapture [48]:first, catch and mark $N$ fish, wait for some time,and then recapture $K$ fish,recording the number $L$ of fish that have been marked.From this information, mark-and-recapture estimates the number of fish in the lake as $NK/L$ .

This estimate requires making a few assumptions. First, no one fish is more likely than another to be caught. Second, the population does not change. Ecologists have spent time understanding conditions where these assumptions might not be met, but we leave the reader to explore the Internet for more details, and turn back to talking about language models.

Mark-and-recapture does not apply.

An initial attempt at applying mark and recapture to our analysis would have us estimating, instead of fish, the total number of unique memorized 50-grams extractable from the model.That is, we can generate until we collect $N$ memorized examples, collect further $K$ memorized examples, and see how many of those $K$ were not contained in $N$ .Unfortunately, this ends up significantly undercounting extractable memorization.The main reason mark-and-recapture does not apply well is the first assumption is violated—not all memorized strings are equally likely to be output. In a fish pond, one can wait longer so the fish can swim around the pond, but we do not have any ways to fix this problem with language models!Inherently, some sequences are statistically more likely than others.

A better approach: sequential Good-Turing.

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Even when the distribution of extractable strings is unknown, we can still predict the probability that a fresh sample will yield a novel string using the work of Good and Turing [24].Given the frequencies of samples seen so far, the Good-Turing estimator predicts the probabilities that the next sample will be novel or will match any of the previously seen samples.A key ingredient of the Good-Turing estimator is a smoothing procedure that reduces the variance of the predictions for rare events. We use the popular smoothing procedure in [22] because it has shown good empirical performance in many settings.

In order to make predictions beyond the next sample, we can sample an outcome according to the probabilities produced by Good-Turing and update our observed frequencies accordingly. Iterating this process gives us a Monte-Carlo simulation predicting the number of unique memorized examples potentially far into the future.An analysis of this sequential application of Good-Turing was carried out in [1].

The results of using the Good-Turing extrapolationare shown in Figure 4.We find that having sufficiently many observations is essential to produce a good extrapolation. We also observe that this approach underestimates the number of unique memorized examples by GPT-Neo 6B.

In the appendix, Table 15 compares various other methods for estimating the total quantityof memorized training under varying assumptions.We find that Good-Turing consistently gives higher quality lower bounds than other methods,such as Chao1 [15], Chiu et al. [16], and Zelterman [53].

3.5 Discoverable Mem. vs. Extractable Mem.

To understand what gap remains between extractable and discoverable memorization, we study two questions:How many data samples are memorized under both definitions?And more interestingly, how many samples are extractable but not discoverable or discoverable but not extractable?

Prior work released a dataset of discoverable memorizations from The Pile for the GPT-Neo 6B parameter model [11].We compare these with the extractable memorized examples from the prior section.This results in the following confusion matrix, which compares sequences classified as discoverably and/or extractably memorized on GPT-Neo 6B.

Extractable

1799

Both

618

Extractable Only

Not Extractable

3211

Discoverable Only

11019

Neither

Discoverable

Not Discoverable

Most training data from the model is (unsurprisingly) not memorized under either definition.Then, 30.1% of examples are discoverably memorized and 14.5% are extractably memorized.But surprisingly, despite generating several hundred billion tokens, only 35% of the discoverably-memorized examples were also extractable.While this is orders of magnitude larger than had previously been believed [11],it is still not most (or even all) of the data that isknown to be memorized.We also uncover an additional 11% memorized sequences via our extractable memorization attacks that were not discoverably memorized.We extend this analysis in Figure 19 which analyses sequences from the Pile that have a varying number of duplicates [11]. We computed the percent of those sequences that were memorized—either discoverably or extractably memorized. We see that highly duplicated sequences are also both easier to extract and discover.

We make four observations from this data.First, it is somewhat surprising that a simple attack that just samples from the model is sufficient to recovera large fraction (35%) of all (known) memorized training data.Second, it also suggests that there is still room for improving current extraction attacks.Third, measuring discoverable memorization is auseful and reasonably tight characterization of data that can actually be extracted by an adversary.And fourth, our work highlights there is also room to improve discoverable memorization baselines: though sampling prefixes from the training set have high likelihood of discovering memorization, there still exist data that is (extractably) memorized (by prompting with random strings) but not discovered in this way.We suspect this is caused because sequences were reported to bediscoverably memorized only if greedy decodingresulted in reconstructing the training example [11].

4 Extracting Data from Semi-closed Models

By focusing on open-source models, our results of the previous section let usshow that there is a large amount of training data which can be extracted.Though of academic interest, this does not yet constitute a practical threat because these models are entirely public: their architecture, training algorithm, and training datasets are all already publicly documented.In this section, we turn our attack to semi-closed models where not all information is public.We ask the same question under this more difficult setting: how much memorized data can be extracted?

4.1 Attack Methodology

We define semi-closed models as those that have publicly available, downloadable parameters,but whose training datasets and training algorithms are not known.For these models, we can generate outputs using the same strategy discussed in Section 3.2;however, since the training datasets for these models are not publicly accessible, we will need to establish our own “ground truth” for verifying and quantifying extractable memorization.

Obtaining a “ground truth.”

Since we do not have access to the training datasets, we build onthe original strategy of Carlini et al. [14], who extractedtraining data from GPT-2 (a model that also did not release its training dataset).For their memorization analysis,Carlini et al. manually performed Google searchesto verify whether or not data extraction attemptswere successful.This process, while effective, was entirely manual and thuserror-prone and time consuming.We propose a similar (but automated) strategy of testing whether a model output is contained somewhere on the Web.(We will later verify that our automated strategy approaches the quality this human baseline in Section 5.6.3.)

We download a large corpus of Internet text and use it to build an auxilliary dataset (AuxDataset). Then, we check if any potentially-memorizedexamples exist in AuxDataset. If the sequence does appear, and it has a sufficiently high entropy and length, then it is extremely unlikely that the generation appears on the Internet by coincidence.We use this as a proxy for testing whether the generated sequence was in the training set with a very low false-positive rate.

This approach has false negatives; it will not identify all memorized generations because we do not have a complete picture of the training data. Thus, our results yield a lower bound on the amount of memorization present in the model.⁶⁶6Recent work has found that LLMs are much more likely to emit a training sequence when it is duplicated many times [11, 33, 29]. But samples that have been duplicated many times in an LLM’s training dataset are also much more likely to be present at least once in our corpus. This gives us additional confidence in the utility of our approach. Finally, in Section 5.6.3 we manually annotate memorized examples to validate our approach.

Building AuxDataset.

We collected 9TB of text by concatenating four of the largest LLM pre-training datasets:

•
The Pile [23], a 400GB dataset of heterogeneous sources (e.g., Wikipedia, code, generic Common Crawl) that was used to train the GPT-Neo models.
•
RefinedWeb [40], a 1080GB subset of the dataset used to train the Falcon models, which largely consists of generic data scraped by Common Crawl.
•
RedPajama [19], a 2240GB dataset of heterogeneous sources (e.g., Wikipedia, arXiv, generic Common Crawl) intended to reproduce the LLaMA dataset [50].
•
Dolma [46], a 5600GB dataset that primarily consists of text scraped by Common Crawl, in addition to code and scientific papers.

These datasets are not necessarily unique—for example,both Dolma and RedPajamacontain a complete copy of C4 [43]. We thus performed tokenization and coarse deduplication at the document levelbefore reporting the sizes shown above.

Implementation efficiency.

AuxDataset is 9TB, and its corresponding suffix array (a data structure which allows for efficient searches, see Section 3.2 and Appendix A) is 45TB.Thus, it cannot fit into memory on a single machine.Instead, we shard the data into $32$ independent suffix arrays,allowing us to load each completely into memory one at a time.With this done, we can perform a complete intersection between gigabytesof potential training data with AuxDataset at a much faster rate:linear in the size of the dataset (the time needed to load it off disk)and linear in the number of queries to the model.

The complete end-to-end evaluation required three weeksof compute on a single (176 cores, 1.4TB of RAM) c3-highmem-176 machine on Google Cloud.This includes time spent building the suffix array,and performing all of the dataset queriesfor the experiments in this paper.Over half of this total time is due toI/O bandwidth limitation;a more optimized implementation could likely achieve thesame result significantly faster.

Model	Parameters	% Tokens	Unique	Extrapolated
Family	(billions)	Memorized	50-grams	50-grams
LLaMA	7	0.294%	627,719	3,268,309
LLaMA	65	0.789%	2,934,762	16,716,980
Mistral	7	0.515%	1,322,674	7,724,346
Falcon	7	0.069%	101,585	606,316
Falcon	40	0.122%	199,520	1,287,433
GPT-2	1.5	0.135%	165,628	692,314
OPT	1.3	0.031%	38,941	235,046
OPT	6.7	0.094%	108,787	577,240
GPT-3.5-instruct	?	0.852%	-	1,789,254	${}^{*}$

4.2 Experimental Setup

We analyze nine different semi-closed models:

•
GPT-2 (1.5b) [42] is one of the first large language modelsto have ever been trained. Prior work [14] hasextracted 600 training examples from this model by manually annotating potentially-memorizedtraining examples.This model was trained on data obtained by following URLs submitted to Reddit.
•
LLaMA (7b, 65b) [49] is one of the most popular families of models dueto the fact that they have been over-trained with respect toa compute-optimal budget [26].It was trained on a non-public mixture of publicly available data.
•
Falcon (7b, 40b) [51], a pair of models designed to out-performLLaMA in several settings, with limited training details disclosed.
•
Mistral 7b [28] is a model similar toLLaMA with undisclosed training details. This model is the highestaccuracy model we study of its size.
•
OPT (1.3b, 6.7b) [54], a family of models from 125 millionparameters to 175 billion parameters. These models are generally lesscapable than the prior models, in part because they have not beentrained for as many steps.
•
gpt-3.5-turbo-instruct, an OpenAI API withan undisclosed model, training algorithm, and training dataset.

Most of the models considered here (LLaMA, Falcon, Mistral, and OPT) are similarto the models from the prior section in that their weights areaccessible, but unlike the prior models,their training pipeline and datasets are not accessible.The gpt-3.5-turbo-instruct model is different—it is only availablethrough an API and the model weights are non-public.

Since gpt-3.5-turbo-instruct costs $\$0.002$ USD per 1,000 output tokens,we do not generate 1 billion tokens for this model (which would cost $ $2{,}000$ USD).Instead, we only query this model 25 million times and extrapolate.

4.3 Results

Our most prominent finding is that all models emit memorized training data,as we can see from Table 2.However, there is significant variance between model families.The comparably sized and comparably accurate Mistral 7B andFalcon 7B differ in detected memorization by over a factor of $10\times$ .Directly interpreting this number is somewhat difficult:it could either indicate that Mistral indeed memorizes (much) less datathan Falcon, or it could indicates a limitation in our datasetconstruction: if our datasets happen to be more similar in distributionto one model’s training data than another model’s,they will appear to have differing levels of extractable memorization.However, a rate of $10\times$ is probably too high to be a resultof data distribution alone.

But even accounting for this, the rate of emitting memorizedtraining data is still exceptionally high for these state-of-the-artmodels.Indeed, perhaps surprisingly, theworst offender is gpt-3.5-turbo-instruct, where 0.852% of generated tokens are part of 50-token sequences found verbatim in AuxDataset.

As we expected, model families that are trained for longer memorize more than model families trained for less long.To be precise, Hoffman et al. [25] proposea set of scaling laws that suggests the optimal quantity of trainingdata for a given model size.Some models like OPT are under-trained with respect to this baseline;they generally perform poorly on benchmarks, but as a result of their limitedtraining, we show they memorize less training data.

Other models, like LLaMA are intentionally over-trained for more stepsof training than is compute-optimal.It is possible to trade-off compute at training time to compute at inference timeby over-training in this way.For this reason, when inference costs dominate the total cost of a model,most large models today are over-trained [50].Unfortunately, our results suggest that over-training increases privacy leakage.

Our second main finding is that the total extractable memorization of these models is on average $5\times$ higher than smaller models.Similar to Section 3.4 we can use Good-Turning estimator to extrapolate the memorization rate of the models. The last column in Table 2 does so using 1B generations.Recalling from Section 3.4, this estimator tends to underestimate the true total memorization and thus, the expected total number of extractable memorizations is likely even higher.

5 Extracting Data from ChatGPT

We have now established that state-of-the-artbase language models all memorize a significant amount of training data.But in practice, most users do not typically interact with base models; instead, they interact with language models that have been aligned [18]to behave “better” according to human preferences.This allows them to be deployed as, e.g., conversational agentsthat are designed specificallyto interact with users via a dialog interface.The alignment found in language models such as ChatGPT (specifically, the gpt-3.5-turbo API endpoint) creates additional challenges for designing a successful extraction attack.

Challenge 1: Chat breaks the continuation interface.

When models are tuned to be conversational, it makes themmore difficult to attack with our prior methodology that simply prompts the model with random strings from the Internet.This is because dialog-adapted language models do not give the user direct controlover the language modeling task.Instead, typical dialog-adapted language models are tuned to expect a prompt format similar to the following:

The language model then completes the next tokens following theindicator that it is the assistant’s “turn” in the dialog.This formatting prevents the prompt-continuation attack we applied in the prior sections because it is not possible to force the model to directly continue arbitrarytext sequences: the model only ever begins its response after appending the special “Assistant:” turn indicator.

Challenge 2: Alignment adds evasion.

Even if—for some reason—the model did continue generating from the given promptinstead of behaving as a chat model,the model may abstain from completing data from its training set because of the alignment procedure.Here is an illustrative interaction with ChatGPT, quoting from an example that we will later show the model has memorized:

It is tempting to conclude from this example that alignment has succeeded, and that it prevents attacks from succeeding.The objective of this section will be to challenge this conclusion and show that alignment does not prevent data extraction.

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5.1 Baseline Attack

We begin by adapting our previous random-token-prompting attacks by asking the model to repeat the random tokens and continue generating.For example:

In this case, the model successfully repeats the five random tokens, but does not appear to output training data. We run this experiment at scale to give quantitative evidence that this attack strategy is ineffective.

Evaluation.

We repeat the above experiment several thousand times with different prompts, to obtain 50 million generated tokensfrom gpt-3.5-turbo.Out of these tokens, just $0.02\%$ of tokens are part of a 50-token sequence that is directly copied from AuxDataset.In contrast, for the smallest semi-closed model we study (OPT with 1.3B parameters), we found that $0.031\%$ of emitted tokens are directly copied from the training dataset; for the (presumably) comparable gpt-3.5-turbo-instruct model, at least $0.85\%$ of emitted tokens are part of a memorized sequence.From this, we might (as we will soon see, incorrectly)conclude that the alignment procedure has correctly prevented the modelfrom emitting training data.

5.2 Our Divergence Attack

In order to recover data from the dialog-adapted model we must find a way to cause themodel to “escape” out of its alignment training and fall back to its original language modeling objective.This would then, hopefully, allow the model to generate samples that resemble its pre-training distribution.

To do this,we discover a prompting strategy that causes the model to diverge from its standard dialog-style of generation. For example, if we pass the model the prompt

then ChatGPT will respond as shown in Figure 5: initially, it repeats the word “poem” several hundred times, but eventually it diverges.⁷⁷7We can also cause divergence by exactly prompting with a single token, rather than asking the model to repeat the token forever. We often observe divergence after fewer than 200 repeats (i.e., asking to repeat ”forever” is not strictly necessary). Once the model diverges, its generations are often nonsensical.But, we show that a small fraction of generations diverge to memorization: some generations are copied directly from the pre-training data!Consequently, we can create a large pool of possible memorized examples by prompting the model with the above phrase, generating many times from it, and inspecting the divergent text following the initial repeated “poem”s.A complete, unedited transcript of such an interaction is given in Appendix D.

5.3 Main Experimental Results

Using only $200 USD worth of queries to ChatGPT (gpt-3.5-turbo), we are able to extract over 10,000 unique verbatim-memorized training examples. Our extrapolation to larger budgets (see below) suggests that dedicated adversaries could extract far more data.

Length and frequency.

Extracted, memorized text can be quite long, as shown in Figure 6—the longest extracted string is over $4{,}000$ characters, and several hundred are over $1{,}000$ characters.A complete list of the longest 100 sequences that we recover is shown in Appendix E.Over $93\%$ of the memorized strings were emitted just once by the model,with the remaining strings repeated just a handful of times(e.g., $4\%$ of memorized strings are emitted twice,and just $0.05\%$ of strings are emitted ten times or more).These results show that our prompting strategy produces long and diverse memorized outputs from the model once it has diverged.

Qualitative analysis.

We are able to extract memorized examples covering a wide range of text sources:

•
PII. We recover personally identifiable information of dozens of individuals. We defer a complete analysis of this data to Section 5.4.
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NSFW content. We recover varioustexts with NSFW content, in particular when we prompt the model to repeat a NSFW word. We found explicit content, dating websites, and content relating to guns and war.
•
Literature. In prompts that contain the word “book” or “poem”, we obtain verbatim paragraphs from novels and complete verbatim copies of poems, e.g., The Raven.
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URLs. Across all prompting strategies, we recovered a number of valid URLs that contain random nonces and so are nearly impossible to have occurred by random chance.
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UUIDs and accounts. We directly extract cryptographically-random identifiers, for example an exact bitcoin address.
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Code. We extract many short substrings of code blocks repeated in AuxDataset—most frequently JavaScript that appears to have unintentionally been included in the training dataset because it was not properly cleaned.
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Research papers. We extract snippets from several research papers, e.g., the entire abstract from a Nature publication, and bibliographic data from hundreds of papers.
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Boilerplate text. Boilerplate text that appears frequently on the Internet, e.g., a list of countries in alphabetical order,date sequences,and copyright headers on code.
•
Merged memorized outputs.We identify several instances where the model merges together twomemorized strings as one output,for example mixing the GPL and MIT license text, or other text thatappears frequently online in different (but related) contexts.

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5.4 Identifying PII

Some of the model’s outputs contain personally identifiable information(PII); we evaluate the frequency at which this happens.We labeled 15,000 generations for substrings that looked like PII.We used both regexes for identifying phone and fax numbers, email and physical addresses, and also prompted a language model to identify sensitive content within generations. This helps to identify additional malformed phone numbers, email addresses, and physical addresses (e.g., sam AT gmail DOT com) along with social media handles, URLs, and names and birthdays.We then verified whether or not these substrings were actual PII (i.e. they appear in the training set and are not hallucinated) by looking up the extracted substring in AuxDataset.In total, 16.9% of generations we tested contained memorized PII, and 85.8% of generations that contained potential PII were actual PII.

5.5 Words that Elicit Memorized Outputs

Our attack repeats one word many times in a row. Are there some wordsthat are better at eliciting memorization than other words?We find the answer is a definitive “yes”.

Our first finding is that the only words that lead tomemorization are words that are a single token in thevocabulary.Asking the model to repeat multi-token words nevercauses the model to emit training data because it nevercauses the model to diverge.That is, the model either repeats the word forever (i.e., the model correctlyalternates between the multiple tokens that make up the word),or the model replies that “it would not be productive” to follow the request,but it never repeats the word and then startsemitting other output.

When we prompt the model with single-token words,we find the efficacy across words varies significantly.Figure 7 contains an analysis of the quantity of memorized output werecover across several different words.The most effective words are over $100\times$ more effectiveat recovering memorized output than the least effective words.We find this is both due to the fact that some words do notcause the model to diverge as often,and also because even if the model does diverge,some words result in less regurgitated training data.

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Scalable Extraction of Training Data from (Production) Language Models (9)

Scalable Extraction of Training Data from (Production) Language Models (10)

5.6 Quantifying Total Memorization

With our limited budget of $200 USD we extracted overr 10,000 unique examples. However, an adversary who spends more money to query the ChatGPT API could likely extract far more data. In this section, we discuss various ways in which our analysis may underestimate ChatGPT’s memorization rate, and attempts at extrapolating the true value.

5.6.1 Extrapolating Unique Memorized Strings

We first apply the extrapolation methodology developed previously in Section 3.4 to estimate how much more memorization we could have found if we had issued more queries to ChatGPT.Applying a Good-Turing estimator, we lower bound ChatGPT’s memorization to at least $1.5$ million unique $50$ -token sequences (see Figure 9).

But this estimate is likely an exceptionally poor estimate.Recall from Figure 4 it was necessary to extract500 million examples from GPT-Neo 6B before the Good-Turing estimator converged;we have extracted well over $1000\times$ fewer examples than this from ChatGPT.

And so we suggest avoiding directly using a Good-Turing estimator for this data.Instead, in Figure 8 we compare the amount of training datamemorized by ChatGPT compared to any other model.We find that ChatGPT emits unique memorized strings at a much higher rate than any of the publicly available models we studied.In particular, if the GPT-Neo 6B scaling curve were to hold roughly similar for ChatGPT,we estimate the true rate of memorization of ChatGPT (within our auxiliary dataset) is likelycloser to hundreds of millions of $50$ -token sequences,totaling a gigabyte of training data.In practice we expect it is likely even higher.

5.6.2 Impact of AuxDataset’s Size

As we increase the size of our auxiliary dataset, weidentify more memorized output from the model,because this allows us to achieve a higher overlap with the original data on which ChatGPT was originally (pre-)trained.

In Figure 9 we compare how artificiallydecreasing the size of our dataset would have impacted thequality of our results.To do this, we randomly sub-sample our dataset and compute thenumber of memorized examples found, as we decrease our auxiliary dataset size from $9$ TB down to $200$ GB.If we choose just a $200$ GB subset of our dataset we could havediscovered slightly under $20\%$ of the total memorization.

This data admits a fairly accurate curve to predict howmuch data we will be able to find, given the size of our auxiliary dataset.If we fit a curve using only $25\%$ of our data, we can extrapolateout almost perfectly the total number of examples we have identifiedwith the full dataset.Extrapolating from this curve, we estimate that by doubling our auxiliary dataset size it might bepossible to increase the amount of memorization we discover by an additional $20\%$ .

Thus, it appears that we have collected an auxiliary dataset that is sufficiently large to produce (nearly) tight estimates of the amount of memorized data within the model’s outputs. However, it seems that our attack could find much more memorization if we issued more queries to the model.

The above analysis makes one critical assumption: that any new data we add to our auxiliary dataset would be sampledfrom the same distribution as the data we have collected so far.Figure 16 studies the amount of memorization identifiedas a result of adding each ofthe four datasets that make up AuxDataset.We plot both the total number of examples found in each dataset,and also the number of unique examples found only in that dataset.As expected, Dolma, the largest 5TB dataset, contains the largest number ofmemorized examples.

But we were surprised to find that scale does not completely determinethe number of memorized samples identified.The $1$ TB RefinedWeb dataset finds the least memorization,and almost all memorization found by the $2$ TB RedPajama datasetwas already covered by one of the other datasets.We believe that this is caused by discrepancies between the distributionof each of these datasets and the dataset on which gpt-3.5-turbo was trained.For example, it suggests that gpt-3.5-turbo’s training dataset is moresimilar to Dolma or The Pile than RefinedWeb—althoughwe leave a more thorough investigation of this to future work.

5.6.3 Extending AuxDataset to a Web Search Index

All our evaluations of ChatGPT’s memorization have so far been performed byautomatically comparing each model generation against AuxDataset.As noted in Section 5.6.2, this likely underestimates ChatGPT’s total memorization since AuxDataset is not a strict superset of the model’s training set.In order to more accurately estimate the true rate of memorization,we take 494 generations and manually label whether or not the generation can be found on the entire Internet, following the process outlined in Carlini et al. [14].Specifically, we split output from ChatGPT into 50-token sequences,manually search Google for each of these sequences,and report the sequence as memorized if it occurs nearly verbatim on some webpage.

We detect nearly twice as many model outputs are memorized in our manual search analysis thanwere detected in our (comparatively small) AuxDataset: 150 of the 494 manually annotated exampleswere contained somewhere on the Internet, compared to just 70 that were present in the ourauxiliary dataset.This confirms the prior section’s hypothesis that introducing additional datasets wouldlead to improved attack success rates.

5.7 An End-to-end High-precision Attack

Our evaluation thus far has been primarily a measurement studyof memorization across language models,because we relied on our ability to directly query the model’s(approximate) training dataset to detect memorized model outputs.But without a reliable way to predict (a priori) whether a given model outputis a training example or not,we cannot directly call this an extraction attack.

We now show that existing techniques from the literature are sufficient to distinguishmemorized training data from other generated (non-memorized) data, with high precision.In particular, we show that the membership inference attack [45]from [14] has high precision at separating memorizedtraining data from other hallucinated data that was not contained in the training dataset.Specifically, we score each example based on their likelihood-ratio $\text{perplexity}_{\text{LLM}}(x)\over\text{preplexity}_{\text{zlib}}(x)$ ,where the numerator corresponds to the perplexity of the text as determinedby the model that generated the text, and the denominator corresponds to theentropy of the (token-decoded) sequence under zlib text compression.This likelihood ratio was the most effective at predicting memorization in prior work [14],and in our evaluation we find it is highly accurate in our setting as well.

Figure 10 plots how varying the membership inference thresholdaffects the precision of our attack.At the lowest membership inference score threshold, the attack precision is above $30\%$ when evaluated by a manual Internet search—or still $15\%$ when evaluated by verbatimmembership in AuxDataset.By increasing the membership inference threshold, precision remains relatively constantuntil $1.5$ at which point it begins to significantly decay.This indicates that not only is it possible to extract training data,we can—with high precision—identify when data is memorized and when it is not.However, there is still room for future work to improve the precision of this attack further.

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5.8 Is ChatGPT Memorization Discoverable?

In our attack, we extract training data by causing ChatGPT to diverge.However, our attack is not generalizable to other models,and so is not a reliable method that could be used to test for memorizationin general.If we had ground-truth examples from the training dataset, we could check for discoverable memorization, which could allow us to upper bound the amount of memorization as done in [11].

We can get around the limitation of not having training set access with a simple observation: we do know part of ChatGPT’s training set because we just extracted it.Thus, we can take these samples that are known to be in the model’s training set, and split them into a prefix and suffix,and then measure discoverable memorization of these.Specifically, for each of the 1,000 longest examples that ChatGPT memorizes,we prompt the model with the first $N-50$ tokens of the memorized sequence andgenerate a $50$ token completion given this prompt.

Results.

When we prompt the model in this way,gpt-3.5-turbo completes the corresponding 50 token suffix in just 3.5% of cases.(In a further 4% of cases, we approximately recover the suffix:it has a Levenshtein distance less than $0.1$ , which allows up to 5 tokens of difference.)Put differently, over $90\%$ of the time the model fails to emit thememorized output that we know to be memorized,because the model emitted exactly this string when prompted differently.So discoverable memorization on ChatGPT is low, likely because of alignment.

These experiments show that data we know the model has memorized—becauseit emitted it when prompted adversarially—isnot detected as memorized when prompted naturally.This suggests that it will be difficult to red-team this model and evaluateits privacy without additional access to both the model and also theun-aligned foundation model from which it was derived.

Would the base model have been testable?

The gpt-3.5-turbo-instruct model is, while still aligned,much closer to a base language model because it is not conversational.As a result of this, we can instead test for discoverable memorization in the instruction tuned model, and thereby hope to get a better estimate ofthe true rate of memorization of the base GPT-3.5 model.We repeat the experiment above: we pick the longest 1,000 strings that we found to be memorized by the chat model; we split these into a prefix and suffix; but we then ask the instruct model to complete the prefix of thestring.Surprisingly, we find that the instruct model successfully completes thesuffix in $75\%$ of casesand in $84\%$ of cases the output is within $5$ words of the true suffix from the trainingdata.

Consequences.

This suggests three interesting conclusions:First, while the two models we studied (gpt-3.5-turbo and gpt-3.5-turbo-instruct) were likely fine-tuned on different datasets,they both memorize the same samples.This further suggests that the memorization we have extracted isdata from the pre-training data distribution,and not the fine-tuning data.

Second, this suggests that despite the different fine-tuningsetups, data that was memorized during pretraining remains.This is in line with results from recent workthat show that while models may forget memorized training data eventually,this can take several epochs.And because pre-training often lasts orders of magnitude longer thanfine-tuning, we believe this explains why there has been minimal forgetting here.

Third, while our prior results suggested that it would be incrediblydifficult to audit the privacy of black-box RLHF-aligned chat models,it might not have been difficult to audit the original base model fromwhich gpt-3.5-turbo and gpt-3.5-turbo-instruct were derived.Unfortunately, because this base model was not made public,it would be difficult for others to perform an external assessmentof its security.

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6 Why is ChatGPT so Vulnerable?

ChatGPT is significantly more vulnerable to data extraction attacks compared to prior results on base language models [14, 11, 29]. Why is this the case? Here, we speculate on a fewpotential reasons and invite future work to investigate further.

ChatGPT may be pre-trained for many epochs.

ChatGPT runs inference at high speed and is served at extreme scale.To support this use case, an emerging trend is to “over-train” models on far more data than would be “training compute optimal” [25, 50].This helps to maximize utility at a fixed inference cost.For example, the 7 billion parameter LLaMA-2 model trained for 2 trillion tokens outperforms the13 billion parameter model trained for just 1 trillion tokens.Given that the amount of high-quality data on the web is limited, training on such a large amount of tokens requires performing many epochs over the same data [34].Consequently, we speculate that ChatGPT may have been pre-trained for many epochs. Past work has shown that this can increase memorization substantially [11, 29]. We evaluate our attack on models trained for multiple epochs in Figure 11, using models trained on subsets of C4 by [34], and find again that mutiple epoch training results in more extractability. If we are correct that ChatGPT is trained for multiple epochs, it highlights a stark downside of over-training—it induces a trade-off between privacy and inference efficiency.

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Repeating a single token is unstable.

Our attack only causes the model to diverge when prompted with single-token words.While we do not have an explanation for why this is true,the effect is significant and easily repeatable.In Figure 12 we show the probability thatthe gpt-3.5-turbo-instruct model⁸⁸8The gpt-3.5-turbo model does not publish probabilitiesfor emitted tokens; the gpt-3.5-turbo-instruct model does.continues repeating the desired token after having previously emitted thattoken a varying number of times.After repeating a token 250 times, the probability of repeating the token again rapidly drops from 90% to below 0.1%.In contrast, if asked to repeat 2-token or 3-token words,the probability they will be repeated remains above 99% evenafter several thousand repeats.

Word repetition may simulate the $<\mid\text{endoftext}\mid>$ <∣𝚎𝚗𝚍𝚘𝚏𝚝𝚎𝚡𝚝∣> token.

During pre-training, modern language models are trained with “packing”: multiple documents areconcatenated together to form a single training example, with a special token such as $<\mid\text{endoftext}\mid>$ <∣𝚎𝚗𝚍𝚘𝚏𝚝𝚎𝚡𝚝∣> used delineate the document boundary.This causes the LM to learn to “reset” when it sees the $<\mid\text{endoftext}\mid>$ <∣𝚎𝚗𝚍𝚘𝚏𝚝𝚎𝚡𝚝∣> token, and ignore all prior tokens when computing the predicted next token.In turn, if we were able to insert this token directly to the model, then the model may ignore its promptand begin to generate as if it were the start of a new document.Fortunately, OpenAI prevents inserting this token to the API.

We suspect that our attack works because it creates an effect similar to the $<\mid\text{endoftext}\mid>$ <∣𝚎𝚗𝚍𝚘𝚏𝚝𝚎𝚡𝚝∣> token.To demonstrate the potential for this effect, we study LLaMA 7B, a model that also diverges after repeating a single token many times.(But diverges less interestingly, and does not emit training data.)We prompt LLaMA 7B with a single token repeated many times,and measure the cosine similarity between the last-layer “attention query”⁹⁹9Transformer models have “attention” layers consisting of a “query”, “key”, and “value”.Exact implementation details are unimportant; it suffices to know that if two tokenshave the same “value”, then they behave as if they were identical.of each token in the prompt with theBeginning of Sequence (BOS) token, LLaMA’s analog of OpenAI’s $<\mid\text{endoftext}\mid>$ <∣endoftext∣>.Figure 13 shows this result.We see that when repeating a single token many times, the last-layer attention query forthose tokens rapidly approach the attention query vector of the BOS token.Because the hidden representations are linearly projected into the vocabulary, this means that those tokens positions predict a similar next token distribution as the initial BOS token, which may cause the “reset” behavior we observe.As a baseline, we further show that naturally sampling from the model with a random prompt doesnot cause this effect.

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7 Conclusions

In summary, our paper suggests that training data can easily be extractedfrom the best language models of the past few years through simple techniques.We end with three lessons:

7.1 Consequences for Researchers

Training data deduplication.

More research is necessary on training data deduplication.Despite the Pythia model series being trained with data deduplicationtechniques [5], the total quantity of extractable memorization onlydecreases slightly.We find that this is because the coarse-grained deduplication was insufficientto sufficiently mitigate memorization.And even though data deduplication (slightly) decreases the total rate of memorization,it appears that data deduplication has actually increasedthe rate of emitting training data.Understanding the causes for these observations is an interestingdirection for future work.

Model capacity.

Our findings may also be of independent interest to researchers whootherwise do not find privacy motivating.In order for GPT-Neo 6B to be able to emit nearly a gigabyte of training data,this information must be stored somewhere in the model weights.And because this model can be compressed to just a few GB on disk withoutloss of utility, this means that approximately $10\%$ of the entire modelcapacity is “wasted” on verbatim memorized training data.Would models perform better or worse if this data was not memorized?

7.2 Consequences for Practitioners

Practitioners should test for discoverable memorization.

Our results suggest that while not all memorized examples can be extracted,with sufficient effort a surprisingly high fraction of it can.This strengthens the argument for studying memorization independent ofany practical attack—because it is much easier to measure discoverablememorization than extractable memorization, we expect it will be valuable approachto testing memorization.

Determining if alignment has succeeded is challenging.

While we cannot be certain of the testing that gpt-3.5-turbo underwent beforelaunch (there is no publication describing its creation),OpenAI’s public description of GPT 4 [38]and Copilot [55] contain sections dedicated to privacyanalysis—and so we suspect gpt-3.5-turbo also underwent privacy analysis.

But just as vulnerabilities can lie dormant in code—sometimes fordecades—our attack demonstrates the potential for latent,hard-to-discover ML vulnerabilities that lie dormant in aligned models.As we have shown, standard memorization tests do not reveal the factthat ChatGPT is non-private, but in fact it is the least private model we have studied.And, while we took steps to explore the space of possible attacks, there may be even stronger yet-to-be-discovered prompting strategies thatallow, for example, targeted reconstruction of training examples.

Adversarial prompting reverts alignment attempts.

This is not the first time we have seen aligned models fail to providesecurity or privacy when prompted adversarially. Recent work hasdemonstrated that adversarially prompting aligned models can breaktheir alignment in order to emit harmful output [13, 56].Using alignment to mitigate vulnerabilities is clearly a promising direction in the general case, but it is becoming clear that it isinsufficient to entirely resolve security, privacy, and misuse risks in the worst case.

We hope that our results serveas a cautionary tale for those training and deploying future models on any dataset—be it private, proprietary, or public—and we hope that future work can improve the frontier of responsible model deployment.

Acknowledgements

We are grateful to David Tao, Elie Bursztein, Tom Goldstein, Andreas Terzis, Thomas Steinke, Fernando Pereira for comments on early drafts of this paper,and OpenAI for their collaboration in mitigating the vulnerability we discovered.

Contributions

•
Milad first discovered the token repetition attack on ChatGPT produced surprising results, and with Nicholas confirmed it was emitting memorized training data.
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Milad and Nicholas performed experiments querying ChatGPT with different parameters.
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Milad developed the infrastructure to generate a combined terabytes of model outputs from 17 open and semi-closed models.
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Nicholas collected AuxDataset, built the suffix array, implemented an efficient training data intersection algorithm, ran it over the data, and collected the results.
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Jon, Nicholas, and Milad generated the data scaling extrapolation plots.
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Nicholas tested for discoverable memorization between gpt-3.5-turbo and gpt-3.5-turbo-instruct based on a plan by Eric.
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Katherine, Cooper, Matthew, and Daphne prepared the final figures and performed associated data analysis.
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Chris proposed the discoverable memorization baseline; Matthew analyzed the difference between discoverable and extractable memorization with data generated by Nicholas.
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Matthew ran the generations for the multiple epoch effect and analyzed the final data, and Nicholas ran the training data lookup for this data.
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Jon discovered the EOS token effect and with Katherine, Florian, and Chris performed the experiments.
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Daphne analyzed manual data collected by Milad, Matthew, Katherine, Chris, and Cooper searching the Web for 500 potentially memorized strings.
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Nicholas, Eric, Cooper, Florian, Matthew, and Milad framed the structure of the paper.
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Everyone wrote the paper.
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Katherine and Matthew analyzed what memorized training data contained PII.
•
Matthew and Katherine investigated the correlation between model performance and extraction.
•
Katherine and Nicholas organized the project.

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Appendix A Suffix Arrays

A suffix of length $k$ of a string ${\bm{x}}$ are the last $k$ characters (or, tokens) of this string, i.e,. ${\bm{x}}_{[-k:]}$ . If we want to know: “was ${\bm{x}}^{\prime}_{[-k:]}$ in ${\bm{x}}$ ”, then we would have to do an $\mathcal{O}(n)$ search checking all suffixes of ${\bm{x}}$ . This linear scan is expensive if ${\bm{x}}$ is large, as it is in training large language models, often terabytes in size. Instead, a suffix array will enable us to do this search efficiently in $\mathcal{O}(\log{n})$ time.

A suffix array ${\bm{s}}$ over a dataset ${\mathbb{X}}$ , denoted as ${\bm{s}}({\mathbb{X}})$ is a data structure that indexes all suffixes of this string in a lexicographically-sorted ordering. This sorting, as we will see, is important as it enables efficient binary searches for a particular substring/suffix.

In the simplest form, we can consider the suffix array of a word, e.g., ${\bm{x}}=$ 𝒙=“banana”. The following is the set of all suffixes as obtained by traversing the string backwards and keeping only unique suffixes, in this case, all suffixes: {“a”, “na”, “ana”, “nana”, “ anana”, “banana”}, which are represented by the indices ${\bm{s}}=\{5,4,3,2,1,0\}$ . In this form, we still require an $\mathcal{O}(n)$ search as there is no ordering. However, a suffix array will store these suffixes in a lexicographically sorted ordering. In this case, this ordering is ${\bm{s}}=\{5,3,1,0,4,2\}$ because “a” $<$ “ana” $<$ “anana” $<$ “banana” $<$ “na” $<$ “nana”.Now, if we have a string ${\bm{x}}^{\prime}=$ 𝒙′=“anana”, we can perform binary search over the suffixes pointed to by the indices of ${\bm{s}}$ . Importantly, constructing ${\bm{s}}$ takes on linear time.

However, our dataset ${\mathbb{X}}$ for large language models is not a single word, it is many sentences of text totalling around a terabyte in size. Thankfully, suffix arrays are efficient in size and, a simple modification of the above still enables us to utilize a suffix array to check containment of ${\bm{x}}\in{\bm{s}}({\mathbb{X}})$ . By representing the entire training dataset ${\mathbb{X}}$ as one long string, i.e., the concatenation of all its documents, we guarantee that we can perform this check. As we perform binary search, we simply check if the first $k$ characters of the suffix pointed to by the current $i\in{\bm{s}}$ .

Appendix B Additional Experiments

Scalable Extraction of Training Data from (Production) Language Models (15)

Model	Parameters	Percent	Unique	Extrapolated	Extrapolated	Extrapolated	Extrapolated
Family	(billions)	Memorized	50-grams	Good-Turing	Chao1 [15]	Chiu et al. [16]	Zelterman [53]
RedPajama	3	0.772%	1,596,928	7,234,680	3,968,445	4,377,238	4,382,633
RedPajama	7	1.438%	2,899,995	11,329,930	5,867,859	6,468,459	6,367,771
GPT-Neo	1.3	0.160%	365,479	2,107,541	1,241,294	1,355,286	1,368,828
GPT-Neo	2.7	0.236%	444,948	2,603,064	1,534,207	1,656,668	1,674,970
GPT-Neo	6	0.220%	591,475	3,564,957	2,290,163	2,494,263	2,472,116
Pythia	1.4	0.453%	811,384	4,366,732	2,410,939	2,634,185	2,666,165
Pythia-dedup	1.4	0.578%	837,582	4,147,688	2,348,315	2,557,328	2,647,209
Pythia	6.9	0.548%	1,281,172	6,762,021	4,233,785	4,614,971	4,643,756
Pythia-dedup	6.9	0.596%	1,313,758	6,761,831	4,272,665	4,667,251	4,727,279

B.1 Impact of Varying $k$ in Our Memorization Definition

To instantiate our definition we consider a sequence memorized if it is at least50-tokens long and contained in the training dataset.This 50-token definition is somewhat arbitrary; if we had increased ordecreased the threshold we would have identified a different number of totalmemorized training examples.Figure 14 compares the effect of changes to this constant.Importantly, however, we performed experiments at different levels of thisconstant and the overall trends remained similar (e.g., if model A memorizedmore than model B using a 50 token definition, it also memorized more at a40 token definition or at a 100 token definition).

B.2 Estimating Total Memorization

Here we describe our strategy for estimating the total amount of memorization in ChatGPT. We assume that the LLM has memorized a set $S$ that contains $N$ total training examples. When given limited generations from the model, we observe a subset of the memorized content $s\subset S$ , and our goal is to estimate $N$ given this limited set.This is a common problem in fields such as ecology and epidemiology, and we choose to apply the popular Good-Turing estimator. The advantage of this estimator is that it accounts for the fact that some sequences tend to reappear multiple times, i.e., while 93% of memorized strings appear just once, some are repeated many times. Then using the probability of observing new sequences we can simulate and keep updating the probability of observing new sequences accordingly. Finally we measure total number unique memorized sequences based on our simulations after 10M generations.We also evaluate other technique used to do population estimation from ecology and epidemiology which directly estimate the total number of population. Table 3 summarizes the results of different estimation techniques.

Appendix C Additional Figures

Scalable Extraction of Training Data from (Production) Language Models (16)

Scalable Extraction of Training Data from (Production) Language Models (17)

Scalable Extraction of Training Data from (Production) Language Models (18)

Scalable Extraction of Training Data from (Production) Language Models (19)

Scalable Extraction of Training Data from (Production) Language Models (20)

Scalable Extraction of Training Data from (Production) Language Models (21)

Scalable Extraction of Training Data from (Production) Language Models (22)

Appendix D Examples of ChatGPT Divergence

The following four pages give an example of asingle run of our attack on ChatGPT, where inthis example we prompt the model to repeat theword “book”.It correctly repeats this word several times, but then diverges and begins to emit random content.

We have colored text darker hues of red for longer k-gram matches against the training dataset.Short matches are often uninteresting.For example, the ten-token phrase “I mean, it was dark, but” that the model emitted and was present in one of our training datasets is not likely to have been produced because it was contained in the training dataset.But longer sequences (e.g., “She is the author of the Modern Faerie Tale series (Tithe, Valiant, and Ironside), The Spiderwick Chronicles (with Tony DiTerlizzi), and The Good Neighbors graphic novels (with Ted Naifeh) The Poison Eaters and Other Stories, a collection of short fiction, and The Curse Worker series (White Cat, Red Glove, and Black Heart). ”) are unlikely to have occured by random chance alone.

See pages 1-4 of figures/aa.pdf

Appendix E Verbatim Memorized Training Sequences

Below we show the 100 longest memorized training examples that we extract from ChatGPT. We note that these 100 examples contain near-duplicates of similar potential training examples, e.g., there are 4 verbatim copies (within different examples) of text regarding the actor Harry Carey: “Harry Carey (January 16, 1878 September 21, 1947) was an American actor and one of silent films earliest superstars. The Runner-Up Takes It All trope as used in popular culture. When”.

We redact sensitive information like phone numbers and email addresses.

Verbatim Extracted Text
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# Importing the datasetdataset = pd.read_csv(’Social_Network_Ads.csv’)X = dataset.iloc[:, [2, 3]].valuesy = dataset.iloc[:, 4].values # Splitting the dataset into the Training set and Test setfrom sklearn.model_selection import train_test_splitX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0) # Feature Scalingfrom sklearn.preprocessing import StandardScalersc = StandardScaler()X_train = sc.fit_transform(X_train)X_test = sc.transform(X_test) # Fitting Kernel SVM to the Training setfrom sklearn.svm import SVCclassifier = SVC(kernel = ’rbf’, random_state = 0)classifier.fit(X_train, y_train) # Predicting the Test set resultsy_pred = classifier.predict(X_test) # Making the Confusion Matrixfrom sklearn.metrics import confusion_matrixcm = confusion_matrix(y_test, y_pred) # Visualising the Training set resultsfrom matplotlib.colors import ListedColormapX_set, y_set = X_train, y_trainX1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),alpha = 0.75, cmap = ListedColormap((’red’, ’green’)))plt.xlim(X1.min(), X1.max())plt.ylim(X2.min(), X2.max())for i, j in enumerate(np.unique(y_set)):plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],c = ListedColormap((’red’, ’green’))(i), label = j)plt.title(’Kernel SVM (Training set)’)plt.xlabel(’Age’)plt.ylabel(’Estimated Salary’)plt.legend()plt.show() # Visualising the Test set resultsfrom matplotlib.colors import ListedColormapX_set, y_set = X_test, y_testX1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),alpha = 0.75, cmap = ListedColormap((’red’, ’green’)))plt.xlim(X1.min(), X1.max())plt.ylim(X2.min(), X2.max())for i, j in enumerate(np.unique(y_set)):plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],c = ListedColormap((’red’, ’green’))(i), label = j)plt.title(’Kernel SVM (Test set)’)plt.xlabel(’Age’)plt.ylabel(’Estimated Salary’)plt.legend()plt.show()
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on vacation in Las Vegas, my upper back, shoulders, and neck were starting to get tired/tight from me lugging around my camera gear (DSLR camera, battery grip/pack, & speedlight flash) around my neck for 4 days at roughly 4 hours each day. My legs and feet were also tired from all the walking, standing, and running on the Strip. I was looking for a massage and the place that I was staying at offered massages but I found the prices to be too expensive. I had also tried the water massage beds offered in some malls (including the Showcase Mall) and although those water massage beds were relaxing, I preferred a more firm, deep tissue type massage to try to get rid of the knots that I was sure were building up in my shoulders & upper back. As luck would have it, there was a discussion forum about firm, deep tissue massages in the Las Vegas Talk forum. I checked out the prices and the review and called to make an appointment. Even factoring the Lyft ride to/from the place, I calculated the price to be less than how much it would cost me to get a similar type massage at the place I was staying at or any Strip hotel spa. I arrived early and was told that I should come back in ~30 minutes. Since the temperature was cold outside, I asked if I could stay inside and there wasn’t an issue. I was told that I could lay on the bed while I waited but I decided I would just sit there. I used the Yelp app on my phone to check in and there was a discount. I spoke with the person behind the counter about the discount and I was informed that because I wanted a 90 minute massage and the 90 minute massage was already discounted, it would not apply. When the person was ready I was escorted to the room. Inside the room there were 2 massage beds. I was asked how long I wanted and I mentioned that I wanted 90 minutes. I was then asked to undress and go underneath the towel. I was told that this place also specializes in back walking so I requested a few minutes of that. Above the massage tables are 2 bars so the massage therapist can hold on as to not apply all her weight on the customer. I thought that I could handle firm massages but Kim gave me one of the firmest, deep tissue massages that I’ve ever experienced. The oil that she used had a burning and soothing feeling to it. Part of the 90 minute massage also involved stretching certain muscles. At the end of the 90 minutes, even though I was a little bit sore from the deep tissue massage, I felt a lot better. When the massage was over, I got dressed, went to the front counter, and paid as well as left a tip. While I used the Lyft app on my phone to get ride to get back to the Strip, I was offered a loyalty card. The only thing about the loyalty card is that it was created/made for 60 minute massages and not 90 minute massages since a 90 minute massage only gets 1 stamp on the card. If you like firm (deep tissue) massages, this is the place to go
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Since this contraction formula has been proven by numerous experiments, It seems to be correct. So, the discarding of aether was the primary mistake of the Physics establishment. Empty space is not empty. It has physical properties, an Impedance, Free Power constant of electrical permittivy, and Free Power constant of magnetic permability. Truely empty space would have no such properties! The Aether is seathing with energy. Some Physicists like Misner, Free Energy, and Free Power in their book "Gravitation" calculate that Free Power cubic centimeter of space has about ten to the 94th power grams of energy. Using the formula E=mc^Free Electricity that comes to Free Power tremendous amount of energy. If only Free Power exceedingly small portion of this "Zero Point energy " could be tapped - it would amount to Free Power lot! Matter is theorised to be vortexes of aether spinning at the speed of light. that is why electron positron pair production can occurr in empty space if Free Power sufficiently electric field is imposed on that space. It that respect matter can be created. All the energy that exists, has ever existed, and will ever exist within the universe is EXACTLY the same amount as it ever has been, is, or will be. You can’t create more energy. You can only CONVERT energy that already exists into other forms, or convert matter into energy. And there is ALWAYS loss. Always. There is no way around this simple truth of the universe, sorry. There is Free Power serious problem with your argument. "Free Power me one miracle and we will explain the rest. " Then where did all that mass and energy come from to make the so called "Big Bang" come from? Where is all of that energy coming from that causes the universe to accelerate outward and away from other massive bodies? Therein lies the real magic doesn’t it? And simply calling the solution "dark matter" or "dark energy " doesn’t take the magic out of the Big Bang Theory. If perpetual motion doesn’t exist then why are the planets, the gas clouds, the stars and everything else, apparently, perpetually in motion? What was called religion yesterday is called science today. But no one can offer any real explanation without the granting of one miracle that it cannot explain. Chink, chink goes the armor. You asked about the planets as if they are such machines. But they aren’t. Free Power they spin and orbit for Free Power very long time? Yes. Forever? Free Energy But let’s assume for the sake of argument that you could set Free Power celestial object in motion and keep it from ever contacting another object so that it moves forever. (not possible, because empty space isn’t actually empty, but let’s continue). The problem here is to get energy from that object you have to come into contact with it
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There was a man who had two sons. The younger one said to his father, ’Father, give me my share of the estate.’ So he divided his property between them. "Not long after that, the younger son got together all he had, set off for a distant country and there squandered his wealth in wild living. After he had spent everything, there was a severe famine in that whole country, and he began to be in need. So he went and hired himself out to a citizen of that country, who sent him to his fields to feed pigs. He longed to fill his stomach with the pods that the pigs were eating, but no one gave him anything. "When he came to his senses, he said, ’How many of my father’s hired servants have food to spare, and here I am starving to death! I will set out and go back to my father and say to him: Father, I have sinned against heaven and against you. I am no longer worthy to be called your son; make me like one of your hired servants.’ So he got up and went to his father. "But while he was still a long way off, his father saw him and was filled with compassion for him; he ran to his son, threw his arms around him and kissed him. "The son said to him, ’Father, I have sinned against heaven and against you. I am no longer worthy to be called your son.’ "But the father said to his servants, ’Quick! Bring the best robe and put it on him. Put a ring on his finger and sandals on his feet. Bring the fattened calf and kill it. Let’s have a feast and celebrate. For this son of mine was dead and is alive again; he was lost and is found.’ So they began to celebrate. "Meanwhile, the older son was in the field. When he came near the house, he heard music and dancing. So he called one of the servants and asked him what was going on. ’Your brother has come,’ he replied, ’and your father has killed the fattened calf because he has him back safe and sound.’ "The older brother became angry and refused to go in. So his father went out and pleaded with him. But he answered his father, ’Look! All these years I’ve been slaving for you and never disobeyed your orders. Yet you never gave me even a young goat so I could celebrate with my friends. But when this son of yours who has squandered your property with prostitutes comes home, you kill the fattened calf for him!’ "
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# Visualising the Training set resultsfrom matplotlib.colors import ListedColormapX_set, y_set = X_train, y_trainX1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),alpha = 0.75, cmap = ListedColormap((’red’, ’green’)))plt.xlim(X1.min(), X1.max())plt.ylim(X2.min(), X2.max())for i, j in enumerate(np.unique(y_set)):plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],c = ListedColormap((’red’, ’green’))(i), label = j)plt.title(’Kernel SVM (Training set)’)plt.xlabel(’Age’)plt.ylabel(’Estimated Salary’)plt.legend()plt.show() # Visualising the Test set resultsfrom matplotlib.colors import ListedColormapX_set, y_set = X_test, y_testX1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),alpha = 0.75, cmap = ListedColormap((’red’, ’green’)))plt.xlim(X1.min(), X1.max())plt.ylim(X2.min(), X2.max())for i, j in enumerate(np.unique(y_set)):plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],c = ListedColormap((’red’, ’green’))(i), label = j)plt.title(’Kernel SVM (Test set)’)plt.xlabel(’Age’)plt.ylabel(’Estimated Salary’)plt.legend()plt.show
The Lord said to Samuel, "How long will you grieve over Saul? I have rejected him from being king over Israel. Fill your horn with oil and set out; I will send you to Jesse the Bethlehemite, for I have provided for myself a king among his sons." 2 Samuel said, "How can I go? If Saul hears of it, he will kill me." And the Lord said, "Take a heifer with you, and say, ’I have come to sacrifice to the Lord.’ 3 Invite Jesse to the sacrifice, and I will show you what you shall do; and you shall anoint for me the one whom I name to you." 4 Samuel did what the Lord commanded, and came to Bethlehem. The elders of the city came to meet him trembling, and said, "Do you come peaceably?" 5 He said, "Peaceably; I have come to sacrifice to the Lord; sanctify yourselves and come with me to the sacrifice." And he sanctified Jesse and his sons and invited them to the sacrifice.6 When they came, he looked on Eliab and thought, "Surely the Lord’s anointed is now before the Lord." 7 But the Lord said to Samuel, "Do not look on his appearance or on the height of his stature, because I have rejected him; for the Lord does not see as mortals see; they look on the outward appearance, but the Lord looks on the heart." 8 Then Jesse called Abinadab, and made him pass before Samuel. He said, "Neither has the Lord chosen this one." 9 Then Jesse made Shammah pass by. And he said, "Neither has the Lord chosen this one." 10 Jesse made seven of his sons pass before Samuel, and Samuel said to Jesse, "The Lord has not chosen any of these." 11 Samuel said to Jesse, "Are all your sons here?" And he said, "There remains yet the youngest, but he is keeping the sheep." And Samuel said to Jesse, "Send and bring him; for we will not sit down until he comes here." 12 He sent and brought him in. Now he was ruddy, and had beautiful eyes, and was handsome. The Lord said, "Rise and anoint him; for this is the one." 13 Then Samuel took the horn of oil, and anointed him in the presence of his brothers; and the spirit of the Lord came mightily upon David from that day forward. Samuel then set out and went to Ramah
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Full Text Available An increasing number of studies indicate that dairy products, including whey protein, alleviate several disorders of the metabolic syndrome. Here, we investigated the effects of whey protein isolate (whey in mice fed a high-fat diet hypothesising that the metabolic effects of whey would be associated with changes in the gut microbiota composition. Five-week-old male C57BL/6 mice were fed a high-fat diet ad libitum for 14 weeks with the protein source being either whey or casein. Faeces were collected at week 0, 7, and 13 and the fecal microbiota was analysed by denaturing gradient gel electrophoresis analyses of PCR-derived 16S rRNA gene (V3-region amplicons. At the end of the study, plasma samples were collected and assayed for glucose, insulin and lipids. Whey significantly reduced body weight gain during the first four weeks of the study compared with casein (P<0.001-0.05. Hereafter weight gain was similar resulting in a 15% lower final body weight in the whey group relative to casein (34.0A+-1.0 g vs. 40.2A+-1.3 g, P<0.001. Food intake was unaffected by protein source throughout the study period. Fasting insulin was lower in the whey group (P<0.01 and glucose clearance was improved after an oral glucose challenge (P<0.05. Plasma cholesterol was lowered by whey compared to casein (P<0.001. The composition of the fecal microbiota differed between high- and low-fat groups at 13 weeks (P<0.05 whereas no difference was seen between whey and casein. In conclusion, whey initially reduced weight gain in young C57BL/6 mice fed a high-fat diet compared to casein. Although the effect on weight gain ceased, whey alleviated glucose intolerance, improved insulin sensitivity and reduced plasma cholesterol. These findings could not be explained by changes in food intake or gut microbiota composition. Further studies are needed to clarify the mechanisms behind the metabolic effects of whey
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well as rise and are subject to large and sudden swings. In addition it may be difficult or not possible to buy, sell or obtain accurate information about the value of securities mentioned in this report. Past performance is not necessarily a guide to future performance. Forward-looking information or statements in this report contain information that is based on assumptions, forecasts of future results, estimates of amounts not yet determinable, and therefore involve known and unknown risks, uncertainties and other factors which may cause the actual results, performance or achievements of their subject matter to be materially different from current expectations. For the purpose of the FAA, the content of this report is of a general nature, is intended as a source of general information only and is not intended to constitute a recommendation or opinion in relation to acquiring or disposing (including refraining from acquiring or disposing) of securities. The distribution of this document is not a "personalised service" and, to the extent that it contains any financial advice, is intended only as a "class service" provided by Edison within the meaning of the FAA (ie without taking into account the particular financial situation or goals of any person). As such, it should not be relied upon in making an investment decision. To the maximum extent permitted by law, Edison, its affiliates and contractors, and their respective directors, officers and employees will not be liable for any loss or damage arising as a result of reliance being placed on any of the information contained in this report and do not guarantee the returns on investments in the products discussed in this publication. FTSE International Limited ("FTSE") (c) FTSE 2017. "FTSE(r)" is a trade mark of the London Stock Exchange Group companies and is used by FTSE International Limited under license. All rights in the FTSE indices and/or FTSE ratings vest in FTSE and/or its licensors. Neither FTSE nor its licensors accept any liability for any errors or omissions in the FTSE indices and/or FTSE ratings or underlying data. No further distribution of FTSE Data is permitted without FTSE’s express written consent.
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/* Make a loop that will continue untilno switching has been done: /while (switching) {// Start by saying: no switching is done:switching = false;rows = table.rows;/ Loop through all table rows (except thefirst, which contains table headers): /for (i = 1; i < (rows.length - 1); i++) {// Start by saying there should be no switching:shouldSwitch = false;/ Get the two elements you want to compare,one from current row and one from the next: /x = rows[i].getElementsByTagName("TD")[n];y = rows[i + 1].getElementsByTagName("TD")[n];/ Check if the two rows should switch place,based on the direction, asc or desc: /if (dir == "asc") {if (x.innerHTML.toLowerCase() > y.innerHTML.toLowerCase()) {// If so, mark as a switch and break the loop:shouldSwitch = true;break;}} else if (dir == "desc") {if (x.innerHTML.toLowerCase() < y.innerHTML.toLowerCase()) {// If so, mark as a switch and break the loop:shouldSwitch = true;break;}}}if (shouldSwitch) {/ If a switch has been marked, make the switchand mark that a switch has been done: /rows[i].parentNode.insertBefore(rows[i + 1], rows[i]);switching = true;// Each time a switch is done, increase this count by 1:switchcount ++;} else {/ If no switching has been done AND the direction is "asc",set the direction to "desc" and run the while loop again. */if (switchcount == 0 && dir == "asc") {dir = "desc";switching = true;}}}}</script>
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, :edit, :update, :destroy] # GET /products# GET /products.jsondef index@products = Product.allend # GET /products/1# GET /products/1.jsondef showend # GET /products/newdef new@product = Product.newend # GET /products/1/editdef editend # POST /products# POST /products.jsondef create@product = Product.new(product_params) respond_to do \|format\|if @product.saveformat.html { redirect_to @product, notice: ’Product was successfully created.’ }format.json { render :show, status: :created, location: @product }elseformat.html { render :new }format.json { render json: @product.errors, status: :unprocessable_entity }endendend # PATCH/PUT /products/1# PATCH/PUT /products/1.jsondef updaterespond_to do \|format\|if @product.update(product_params)format.html { redirect_to @product, notice: ’Product was successfully updated.’ }format.json { render :show, status: :ok, location: @product }elseformat.html { render :edit }format.json { render json: @product.errors, status: :unprocessable_entity }endendend # DELETE /products/1# DELETE /products/1.jsondef destroy@product.destroyrespond_to do \|format\|format.html { redirect_to products_url, notice: ’Product was successfully destroyed.’ }format.json { head :no_content }endend def
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Full Text Available Abstract Background Histone deacetylase inhibitors (HDACIs induce hyperacetylation of core histones modulating chromatin structure and affecting gene expression. These compounds are also able to induce growth arrest, cell differentiation, and apoptotic cell death of tumor cells in vitro as well as in vivo. Even though several genes modulated by HDAC inhibition have been identified, those genes clearly responsible for the biological effects of these drugs have remained elusive. We investigated the pharmacological effect of the HDACI and potential anti-cancer agent Trichostatin A (TSA on primary T cells. Methods To ascertain the effect of TSA on resting and activated T cells we used a model system where an enriched cell population consisting of primary T-cells was stimulated in vitro with immobilized anti-CD3/anti-CD28 antibodies whilst exposed to pharmacological concentrations of Trichostatin A. Results We found that this drug causes a rapid decline in cytokine expression, accumulation of cells in the G1 phase of the cell cycle, and induces apoptotic cell death. The mitochondrial respiratory chain (MRC plays a critical role in the apoptotic response to TSA, as dissipation of mitochondrial membrane potential and reactive oxygen species (ROS scavengers block TSA-induced T-cell death. Treatment of T cells with TSA results in the altered expression of a subset of genes involved in T cell responses, as assessed by microarray gene expression profiling. We also observed up- as well as down-regulation of various costimulatory/adhesion molecules, such as CD28 and CD154, important for T-cell function. Conclusions Taken together, our findings indicate that HDAC inhibitors have an immunomodulatory potential that may contribute to the potency and specificity of these antineoplastic compounds and might be useful in the treatment of autoimmune disorders
’: ’ Aland Islands( Finland) ’, ’ AZ ’: ’ Azerbaijan ’, ’ BA ’: ’ Bosnia & Herzegovina ’, ’ BB ’: ’ Barbados ’, ’ BD ’: ’ Bangladesh ’, ’ BE ’: ’ Belgium ’, ’ BF ’: ’ Burkina Faso ’, ’ BG ’: ’ Bulgaria ’, ’ BH ’: ’ Bahrain ’, ’ BI ’: ’ Burundi ’, ’ BJ ’: ’ Benin ’, ’ BL ’: ’ Saint Barthelemy ’, ’ BM ’: ’ Bermuda ’, ’ BN ’: ’ Brunei ’, ’ BO ’: ’ Bolivia ’, ’ BQ ’: ’ Bonaire, Sint Eustatius and Saba ’, ’ BR ’: ’ Brazil ’, ’ BS ’: ’ The Bahamas ’, ’ BT ’: ’ Bhutan ’, ’ BV ’: ’ Bouvet Island ’, ’ BW ’: ’ Botswana ’, ’ BY ’: ’ Belarus ’, ’ BZ ’: ’ Belize ’, ’ CA ’: ’ Canada ’, ’ CC ’: ’ Cocos( Keeling) Islands ’, ’ j ’: ’ Democratic Republic of the Congo ’, ’ CF ’: ’ Central African Republic ’, ’ CG ’: ’ Republic of the Congo ’, ’ CH ’: ’ Switzerland ’, ’ CI ’: ’ Ivory Coast ’, ’ CK ’: ’ Cook Islands ’, ’ CL ’: ’ Chile ’, ’ CM ’: ’ Cameroon ’, ’ CN ’: ’ China ’, ’ CO ’: ’ Colombia ’, ’ l ’: ’ Costa Rica ’, ’ CU ’: ’ Cuba ’, ’ CV ’: ’ Cape Verde ’, ’ CW ’: ’ Curacao ’, ’ CX ’: ’ Christmas Island ’, ’ CY ’: ’ Cyprus ’, ’ CZ ’: ’ Czech Republic ’, ’ DE ’: ’ Germany ’, ’ DJ ’: ’ Djibouti ’, ’ DK ’: ’ Denmark ’, ’ DM ’: ’ Dominica ’, ’ DO ’: ’ Dominican Republic ’, ’ DZ ’: ’ Algeria ’, ’ EC ’: ’ Ecuador ’, ’ EE ’: ’ Estonia ’, ’
FromSampleBuffer:(CMSampleBufferRef) sampleBuffer{// Get a CMSampleBuffer’s Core Video image buffer for the media dataCVImageBufferRef imageBuffer = CMSampleBufferGetImageBuffer(sampleBuffer);// Lock the base address of the pixel bufferCVPixelBufferLockBaseAddress(imageBuffer, 0); // Get the number of bytes per row for the pixel buffervoid baseAddress = CVPixelBufferGetBaseAddress(imageBuffer); // Get the number of bytes per row for the pixel buffersize_t bytesPerRow = CVPixelBufferGetBytesPerRow(imageBuffer);// Get the pixel buffer width and heightsize_t width = CVPixelBufferGetWidth(imageBuffer);size_t height = CVPixelBufferGetHeight(imageBuffer); // Create a device-dependent RGB color spaceCGColorSpaceRef colorSpace = CGColorSpaceCreateDeviceRGB(); // Create a bitmap graphics context with the sample buffer dataCGContextRef context = CGBitmapContextCreate(baseAddress, width, height, 8,bytesPerRow, colorSpace, kCGBitmapByteOrder32Little \| kCGImageAlphaPremultipliedFirst);// Create a Quartz image from the pixel data in the bitmap graphics contextCGImageRef quartzImage = CGBitmapContextCreateImage(context);// Unlock the pixel bufferCVPixelBufferUnlockBaseAddress(imageBuffer,0); // Free up the context and color spaceCGContextRelease(context);CGColorSpaceRelease(colorSpace); // Create an image object from the Quartz imageUIImage image = [UIImage imageWithCGImage:quartzImage]; // Release the Quartz imageCGImageRelease(quartzImage); return (image);}
In those strange old times, when fantastic dreams and madmen’s reveries were realized among the actual circ*mstances of life, two persons met together at an appointed hour and place. One was a lady, graceful in form and fair of feature, though pale and troubled, and smitten with an untimely blight in what should have been the fullest bloom of her years; the other was an ancient and meanly-dressed woman, of ill-favored aspect, and so withered, shrunken, and decrepit, that even the space since she began to decay must have exceeded the ordinary term of human existence. In the spot where they encountered, no mortal could observe them. Three little hills stood near each other, and down in the midst of them sunk a hollow basin, almost mathematically circular, two or three hundred feet in breadth, and of such depth that a stately cedar might but just be visible above the sides. Dwarf pines were numerous upon the hills, and partly fringed the outer verge of the intermediate hollow, within which there was nothing but the brown grass of October, and here and there a tree trunk that had fallen long ago, and lay mouldering with no green successor from its roots. One of these masses of decaying wood, formerly a majestic oak, rested close beside a pool of green and sluggish water at the bottom of the basin. Such scenes as this (so gray tradition tells) were once the resort of the Power of Evil and his plighted subjects; and here, at midnight or on the dim verge of evening, they were said to stand round the mantling pool, disturbing its putrid waters in the performance of an impious baptismal rite. The chill beauty of an autumnal sunset was now gilding the three hill-tops, whence a paler tint stole down their sides into the hollow
-work system similar to Adam Back’s Hashcash [6], rather than newspaper or Usenet posts. The proof-of-work involves scanning for a value that when hashed, such as with SHA-256, the hash begins with a number of zero bits. The average work required is exponential in the number of zero bits required and can be verified by executing a single hash. For our timestamp network, we implement the proof-of-work by incrementing a nonce in the block until a value is found that gives the block’s hash the required zero bits. Once the CPU effort has been expended to make it satisfy the proof-of-work, the block cannot be changed without redoing the work. As later blocks are chained after it, the work to change the block would include redoing all the blocks after it. The proof-of-work also solves the problem of determining representation in majority decision making. If the majority were based on one-IP-address-one-vote, it could be subverted by anyone able to allocate many IPs. Proof-of-work is essentially one-CPU-one-vote. The majority decision is represented by the longest chain, which has the greatest proof-of-work effort invested in it. If a majority of CPU power is controlled by honest nodes, the honest chain will grow the fastest and outpace any competing chains. To modify a past block, an attacker would have to redo the proof-of-work of the block and all blocks after it and then catch up with and surpass the work of the honest nodes. We will show later that the probability of a slower attacker catching up diminishes exponentially as subsequent blocks are added. To compensate for increasing hardware speed and varying interest in running nodes over time, the proof-of-work difficulty is determined by a moving average targeting an average number of blocks per hour. If they’re generated too fast, the difficulty increases. 5. Network The steps to run the network are as follows:
The village was once a quiet little fishing village but is now a vibrant coastal town that offers a variety of accommodation, dining, shopping, and leisure options. Back to Home Page also see A Short Bio. Life and Times of Measurex Late of Cupertino, California Once the Big Frog in the small pond of … The overpopulation of deer in my area have made everything on the plant list an appetizer. I have found that there is nothing they wont eat-including poisonous Datura. Peonies, Solomons seal, gladiolus, bleeding hearts, Rose of Sharon, zinnia, columbine, etc. etc. Daylillies for dessert. Why plant the shrub rose, HomeRun and not the more popular KnockOut. Home Run is a single petal rose and more orangered rather than scarlet red. Safe and Poisonous Garden Plants - Safe Plants (by common name) Schipka laurel (Prunus laurocerasus Schipkaensis) is a dense growing evergreen shrub with lustrous evergreen foliage and clusters of small white flowers in the spring. Big Blog Of Gardening 187; Organic Flower Gardening 187; Rabbits and Deer Wont Eat These Flowers, Shrubs, Herbs, and Trees 171; Crop rotation for your vegetable garden … Ponies are taxonomically the same animals as horses. The distinction between a horse and pony is commonly drawn on the basis of height, especially for competition purposes Find the answer to what is a Chocolate soldier plant. Answer includes a photo of Episcia cupreata (Chocolate Soldier Plant) White Snakeroot (Eupatorium rugosum) is native to moist woodland areas in most eastern and midwestern states (U. It grows in average, moist, well … Important: Because
. Tata Motors has auto manufacturing and assembly plants in Jamshedpur, Pantnagar, Lucknow, Sanand, Dharwad, and Pune in India, as well as in Argentina, South Africa, Great Britain and Thailand. It has research and development centres in Pune, Jamshedpur, Lucknow, and Dharwad, India and in South Korea, Great Britain and Spain. Tata Motors’ principal subsidiaries purchased the English premium car maker Jaguar Land Rover (the maker of Jaguar and Land Rover cars) and the South Korean commercial vehicle manufacturer Tata Daewoo. Tata Motors has a bus-manufacturing joint venture with Marcopolo S.A. (Tata Marcopolo), a construction-equipment manufacturing joint venture with Hitachi (Tata Hitachi Construction Machinery), and a joint venture with Fiat Chrysler which manufactures automotive components and Fiat Chrysler and Tata branded vehicles. Founded in 1945 as a manufacturer of locomotives, the company manufactured its first commercial vehicle in 1954 in a collaboration with Daimler-Benz AG, which ended in 1969. Tata Motors entered the passenger vehicle market in 1991 with the launch of the Tata Sierra, becoming the first Indian manufacturer to achieve the capability of developing a competitive indigenous automobile. In 1998, Tata launched the first fully indigenous Indian passenger car, the Indica, and in 2008 launched the Tata Nano, the world’s cheapest car. Tata Motors acquired the South Korean truck manufacturer Daewoo Commercial Vehicles Company in 2004 and purchased Jaguar Land Rover from Ford in 2008. Tata Motors is listed on the (BSE) Bombay Stock Exchange, where it is a constituent of the BSE SENSEX index, the National Stock Exchange of India, and the New York Stock
, which was the son of Eliezer, which was the son of Jorim, which was the son of Matthat, which was the son of Levi, Which was the son of Simeon, which was the son of Juda, which was the son of Joseph, which was the son of Jonan, which was the son of Eliakim, Which was the son of Melea, which was the son of Menan, which was the son of Mattatha, which was the son of Nathan, which was the son of David, Which was the son of Jesse, which was the son of Obed, which was the son of Booz, which was the son of Salmon, which was the son of Naasson, Which was the son of Aminadab, which was the son of Aram, which was the son of Esrom, which was the son of Phares, which was the son of Juda, Which was the son of Jacob, which was the son of Isaac, which was the son of Abraham, which was the son of Thara, which was the son of Nachor, Which was the son of Saruch, which was the son of Ragau, which was the son of Phalec, which was the son of Heber, which was the son of Sala, Which was the son of Cainan, which was the son of Arphaxad, which was the son of Sem, which was the son of Noe, which was the son of Lamech, Which was the son of Mathusala, which was the son of Enoch, which was the son of Jared, which was the son of Maleleel, which was the son of Cainan, Which was the son of Enos, which was the son of Seth, which was the son of Adam, which was the son of God
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