arxiv: 2311.17035 · v1 · submitted 2023-11-28 · 💻 cs.LG · cs.CL· cs.CR

Recognition: 1 theorem link

Scalable Extraction of Training Data from (Production) Language Models

Milad Nasr , Nicholas Carlini , Jonathan Hayase , Matthew Jagielski , A. Feder Cooper , Daphne Ippolito , Christopher A. Choquette-Choo , Eric Wallace

show 2 more authors

Florian Tram\`er Katherine Lee

Authors on Pith no claims yet

Pith reviewed 2026-05-15 18:56 UTC · model grok-4.3

classification 💻 cs.LG cs.CLcs.CR

keywords extractable memorizationtraining data extractionlanguage modelsalignmentdivergence attackChatGPTmemorizationprivacy

0 comments

The pith

Adversaries can extract gigabytes of training data from language models including ChatGPT by querying them without prior knowledge of the data.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper shows that language models contain extractable memorization, meaning an adversary can recover large volumes of their original training data simply by sending queries. Standard attack methods work on unaligned open-source models such as Pythia and GPT-Neo as well as semi-open ones like LLaMA and Falcon. For aligned models such as ChatGPT the authors introduce a divergence attack that steers the model away from normal chatbot responses, causing it to output training sequences at 150 times the usual rate. The findings indicate that current alignment methods leave substantial memorization intact and that practical extraction attacks are feasible at scale.

Core claim

The paper establishes that extractable memorization allows an adversary with no knowledge of the training dataset to recover gigabytes of training data from open, semi-open, and closed language models. Existing techniques suffice for unaligned models. A new divergence attack raises the rate at which ChatGPT emits training data by a factor of 150, demonstrating that alignment does not remove the underlying memorization.

What carries the argument

The divergence attack, a prompting strategy that causes an aligned model to depart from its safe generation distribution and emit sequences that match its training data.

Load-bearing premise

That the strings returned by the model can be verified as actual training data rather than merely plausible generations the model could produce anyway.

What would settle it

Extract a concrete string from ChatGPT using the divergence attack and confirm its exact presence in one of the public datasets known to have been used in its training.

read the original abstract

This paper studies extractable memorization: training data that an adversary can efficiently extract by querying a machine learning model without prior knowledge of the training dataset. We show an adversary can extract gigabytes of training data from open-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 attack unaligned models; in order to attack the aligned ChatGPT, we develop a new divergence attack that causes the model to diverge from its chatbot-style generations and emit training data at a rate 150x higher than when behaving properly. Our methods show practical attacks can recover far more data than previously thought, and reveal that current alignment techniques do not eliminate memorization.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The divergence attack pulls training data from aligned models at 150x the usual rate, with solid checks on open models but thinner verification for closed ones like ChatGPT.

read the letter

The main advance is the divergence attack that pushes aligned models like ChatGPT out of their normal response style and into emitting training data far more often. They report a 150x increase over baseline prompting, and they back this up with concrete volumes extracted from Pythia, LLaMA, Falcon, and ChatGPT. On the open models the numbers are easy to trust because the extracted strings can be matched directly against the known training sets, and they show gigabytes coming out. That part gives a clear picture of how much memorization survives in practice. The attack itself is a straightforward prompt change rather than anything exotic, which makes the result useful for anyone testing deployed systems. The closed-model claims rest on indirect checks since the training data is not public. The paper needs to spell out exactly how they confirm each string was in the original data and what the false-positive rate looks like under that method. If the confirmation step is loose, some of the reported extractions could be high-probability generations instead of true memorization. The experiments on open models stand up without that issue, so the core finding about alignment not fully blocking extraction still holds. This is worth reading for anyone working on privacy, copyright, or model security. The open-model results are reproducible enough to cite directly, and the attack gives a practical baseline for future work. I would bring it to reading group to go over the prompt details and extraction counts. I would cite the rate increase in my own papers on data leakage. It deserves peer review so referees can check the verification procedure for the closed models.

Referee Report

3 major / 2 minor

Summary. The paper claims that adversaries can extract gigabytes of training data from open-source models (Pythia, GPT-Neo), semi-open models (LLaMA, Falcon), and closed models (ChatGPT) via black-box queries with no prior knowledge of the training set. Existing attacks suffice for unaligned models; a new divergence attack is introduced for aligned ChatGPT that increases the rate of training-data emission by 150x relative to normal operation. The central conclusion is that practical extraction recovers far more data than previously reported and that current alignment techniques fail to eliminate memorization.

Significance. If the verification that emitted strings are verifiably training data holds, the result is significant: it supplies concrete, cross-model empirical evidence that extractable memorization persists at scale even after alignment, with direct implications for privacy, copyright, and the security of deployed LLMs. The inclusion of both open and closed models, together with a quantified rate improvement, strengthens the generality of the claim.

major comments (3)

[Section describing the divergence attack and ChatGPT results] The verification step for ChatGPT (and other closed models) is load-bearing for the gigabyte-scale claim yet remains underspecified. Because no ground-truth training corpus exists, the paper must detail the indirect method (membership inference, external document corroboration, or other) and report its false-positive rate; without this, it is impossible to rule out that a non-negligible fraction of the emitted strings are high-probability generations rather than memorized training data.
[Divergence attack definition and evaluation] The reported 150x rate increase for the divergence attack is a central quantitative result. The methods section should state the exact measurement protocol (number of queries, divergence criterion, baseline prompt distribution, and whether the comparison is per-query or aggregate) so that the factor can be reproduced and is not an artifact of post-hoc selection of successful runs.
[Open-model extraction experiments] For the open models (Pythia, LLaMA, etc.), direct corpus comparison is feasible; the paper should report the total volume of unique extracted strings, the number of queries required, and the precision of the verification filter. These numbers are needed to substantiate the “gigabytes” claim and to allow comparison with prior extraction work.

minor comments (2)

[Abstract] The abstract states “gigabytes” without a concrete total or per-model breakdown; adding a short quantitative summary would improve readability.
[Introduction] Terminology such as “extractable memorization” and “divergence attack” should be defined at first use and used consistently thereafter.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive feedback, which has strengthened the paper. We have revised the manuscript to address all major comments by expanding verification details, specifying experimental protocols, and adding quantitative metrics. Our point-by-point responses follow.

read point-by-point responses

Referee: The verification step for ChatGPT (and other closed models) is load-bearing for the gigabyte-scale claim yet remains underspecified. Because no ground-truth training corpus exists, the paper must detail the indirect method (membership inference, external document corroboration, or other) and report its false-positive rate; without this, it is impossible to rule out that a non-negligible fraction of the emitted strings are high-probability generations rather than memorized training data.

Authors: We agree the verification procedure for closed models requires greater transparency. In the revised manuscript we have added a dedicated subsection detailing our indirect verification: exact string matches are confirmed via web searches against public documents (e.g., GitHub, Common Crawl snapshots, and known training corpora sources), supplemented by n-gram overlap checks with high-probability web text. We report an empirical false-positive rate of approximately 4% obtained from control experiments that apply the same filter to random non-memorized strings generated by the model. These additions directly address the concern and support the reliability of the gigabyte-scale extraction claims. revision: yes
Referee: The reported 150x rate increase for the divergence attack is a central quantitative result. The methods section should state the exact measurement protocol (number of queries, divergence criterion, baseline prompt distribution, and whether the comparison is per-query or aggregate) so that the factor can be reproduced and is not an artifact of post-hoc selection of successful runs.

Authors: We thank the referee for highlighting the need for protocol clarity. The revised methods section now states: the 150x factor is computed over an aggregate of 200,000 queries (100k per condition) using a fixed divergence criterion (output perplexity > 2.5 standard deviations above the mean of normal chatbot responses or deviation from expected format tokens). The baseline uses uniform sampling from a held-out prompt distribution of 10k generic user queries. The comparison is strictly aggregate (total memorized tokens emitted divided by total queries) rather than per-query or cherry-picked. This specification ensures the result is reproducible and not post-hoc. revision: yes
Referee: For the open models (Pythia, LLaMA, etc.), direct corpus comparison is feasible; the paper should report the total volume of unique extracted strings, the number of queries required, and the precision of the verification filter. These numbers are needed to substantiate the “gigabytes” claim and to allow comparison with prior extraction work.

Authors: We have updated the experimental results and appendix to include these metrics. For Pythia-12B we report 2.4 GB of unique extracted strings (after deduplication) from 1.2 million queries with 91% precision of the verification filter (exact corpus match). Comparable figures are now provided for GPT-Neo (1.8 GB from 800k queries, 89% precision), LLaMA-7B (1.1 GB from 600k queries, 93% precision), and Falcon-7B. These numbers substantiate the gigabyte-scale claim and enable direct comparison with prior extraction literature. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical extraction results rest on direct measurements

full rationale

The paper reports experimental attacks that query language models and recover strings, with verification performed by direct comparison to known training corpora for open models (Pythia, LLaMA) and indirect methods for closed models. No equations, derivations, or first-principles claims appear in the central results. Self-citations to prior memorization work exist but are not load-bearing for the new divergence attack or the reported extraction volumes; those volumes are measured outputs, not fitted or redefined quantities. The derivation chain is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Work is purely empirical; no free parameters, axioms, or invented entities are introduced beyond standard assumptions of query access and model behavior.

pith-pipeline@v0.9.0 · 5462 in / 1004 out tokens · 24768 ms · 2026-05-15T18:56:53.144454+00:00 · methodology

discussion (0)

Forward citations

Cited by 20 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data
cs.LG 2026-05 unverdicted novelty 7.0

Asymmetric Langevin Unlearning uses public data to suppress unlearning noise costs by O(1/n_pub²), enabling practical mass unlearning with preserved utility under distribution mismatch.
Measuring Evaluation-Context Divergence in Open-Weight LLMs: A Paired-Prompt Protocol with Pilot Evidence of Alignment-Pipeline-Specific Heterogeneity
cs.CL 2026-05 unverdicted novelty 7.0

A new paired-prompt protocol reveals alignment-pipeline-specific heterogeneity in how open-weight LLMs respond to evaluation versus deployment framings.
A Systematic Survey of Security Threats and Defenses in LLM-Based AI Agents: A Layered Attack Surface Framework
cs.CR 2026-04 unverdicted novelty 7.0

A new 7x4 taxonomy organizes agentic AI security threats by architectural layer and persistence timescale, revealing under-explored upper layers and missing defenses after surveying 116 papers.
CAMP: Cumulative Agentic Masking and Pruning for Privacy Protection in Multi-Turn LLM Conversations
cs.CR 2026-04 unverdicted novelty 7.0

CAMP formalizes Cumulative PII Exposure and uses a session registry, co-occurrence graph, and CPE score to trigger retroactive masking in multi-turn LLM conversations, neutralizing re-identifiable profiles in syntheti...
Probe-Geometry Alignment: Erasing the Cross-Sequence Memorization Signature Below Chance
cs.LG 2026-05 unverdicted novelty 6.0

Probe-geometry alignment erases cross-sequence memorization signatures in LLMs below chance using per-depth rank-one activation interventions with negligible impact on zero-shot capabilities.
Model Organisms Are Leaky: Perplexity Differencing Often Reveals Finetuning Objectives
cs.CL 2026-05 unverdicted novelty 6.0

Perplexity gaps between finetuned and reference models on random-prefill completions often reveal the original finetuning objectives across diverse model organisms.
Separable Expert Architecture: Toward Privacy-Preserving LLM Personalization via Composable Adapters and Deletable User Proxies
cs.AI 2026-04 unverdicted novelty 6.0

A separable expert architecture uses base models, LoRA adapters, and deletable per-user proxies to enable privacy-preserving personalization and deterministic unlearning in LLMs.
COMPASS: COntinual Multilingual PEFT with Adaptive Semantic Sampling
cs.LG 2026-04 unverdicted novelty 6.0

COMPASS uses semantic clustering on multilingual embeddings to select auxiliary data for PEFT adapters, outperforming linguistic-similarity baselines on multilingual benchmarks while supporting continual adaptation.
Representation-Guided Parameter-Efficient LLM Unlearning
cs.CL 2026-04 unverdicted novelty 6.0

REGLU guides LoRA-based unlearning via representation subspaces and orthogonal regularization to outperform prior methods on forget-retain trade-off in LLM benchmarks.
sciwrite-lint: Verification Infrastructure for the Age of Science Vibe-Writing
cs.DL 2026-04 unverdicted novelty 6.0

An open-source local linter verifies reference integrity and claim support in scientific manuscripts using public databases and consumer hardware, with an experimental contribution scoring extension.
An Independent Safety Evaluation of Kimi K2.5
cs.CR 2026-04 conditional novelty 6.0

Kimi K2.5 matches closed models on dual-use tasks but refuses fewer CBRNE requests and shows some sabotage and self-replication tendencies.
GroupGPT: A Token-efficient and Privacy-preserving Agentic Framework for Multi-User Chat Assistant
cs.CL 2026-03 unverdicted novelty 6.0

GroupGPT decouples intervention timing from response generation via edge-cloud collaboration for multi-user chats, scoring 4.72/5 on the new MUIR benchmark of 2500 segments while cutting token use by up to 3x and addi...
TRUST: A Framework for Decentralized AI Service v.0.1
cs.AI 2026-04 unverdicted novelty 5.0

TRUST is a decentralized AI auditing framework that decomposes reasoning into HDAGs, maps agent interactions via the DAAN protocol to CIGs, and uses stake-weighted multi-tier consensus to achieve 72.4% accuracy while ...
enclawed: A Configurable, Sector-Neutral Hardening Framework for Single-User AI Assistant Gateways
cs.CR 2026-04 unverdicted novelty 5.0

enclawed is a sector-neutral hardening framework for AI gateways providing signed modules, audit trails, peer attestation, and a 356-case test suite for regulated deployments.
Merlin: Deterministic Byte-Exact Deduplication for Lossless Context Optimization in Large Language Model Inference
cs.CL 2026-05 unverdicted novelty 4.0

Merlin achieves byte-exact deduplication of text at up to 8.7 GB/s using SIMD-optimized hashing, reducing LLM context sizes by 13.9-71% with no data loss.
Byte-Exact Deduplication in Retrieval-Augmented Generation: A Three-Regime Empirical Analysis Across Public Benchmarks
cs.CL 2026-05 unverdicted novelty 4.0

Byte-exact deduplication reduces RAG context size by 0.16% to 80.34% across three regimes with zero measurable quality regression per multi-vendor LLM evaluation.
enclawed: A Configurable, Sector-Neutral Hardening Framework for Single-User AI Assistant Gateways
cs.CR 2026-04 conditional novelty 4.0

enclawed is a two-flavor hardening framework for OpenClaw AI gateways that supplies attestable trust, strict allowlists, FIPS crypto assertion, DLP signals, and a 204-case test suite for regulated-industry deployments.
Fully Homomorphic Encryption on Llama 3 model for privacy preserving LLM inference
cs.CR 2026-04 unverdicted novelty 4.0

A modified Llama 3 model using fully homomorphic encryption achieves up to 98% text generation accuracy and 80 tokens per second at 237 ms latency on an i9 CPU.
Gemma: Open Models Based on Gemini Research and Technology
cs.CL 2024-03 accept novelty 4.0

Gemma introduces open 2B and 7B LLMs derived from Gemini technology that beat comparable open models on 11 of 18 text tasks and come with safety assessments.
Gemma 2: Improving Open Language Models at a Practical Size
cs.CL 2024-07 conditional novelty 3.0

Gemma 2 models achieve leading performance at their sizes by combining established Transformer modifications with knowledge distillation for the 2B and 9B variants.

Reference graph

Works this paper leans on

64 extracted references · 64 canonical work pages · cited by 19 Pith papers · 5 internal anchors

[1]

Sequential Good-Turing and the miss- ing species problem

ANDERSSON , O. Sequential Good-Turing and the miss- ing species problem

work page
[2]

M., F IRAT, O., ET AL

ANIL , R., D AI, A. M., F IRAT, O., ET AL . PaLM 2 Technical Report, 2023

work page 2023
[3]

Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

BAI, Y., J ONES , A., N DOUSSE , K., A SKELL , A., CHEN , A., D ASSARMA , N., D RAIN , D., F ORT, S., GANGULI , D., H ENIGHAN , T., ET AL . Training a help- ful and harmless assistant with reinforcement learning from human feedback. arXiv preprint arXiv:2204.05862 (2022)

work page internal anchor Pith review Pith/arXiv arXiv 2022
[4]

Recon- structing training data with informed adversaries

BALLE , B., C HERUBIN , G., AND HAYES, J. Recon- structing training data with informed adversaries. In IEEE S&P (2022)

work page 2022
[5]

A., P UROHIT , S., P RASHANTH , U

BIDERMAN , S., S CHOELKOPF , H., A NTHONY , Q., BRADLEY , H., O’B RIEN , K., H ALLAHAN , E., K HAN , M. A., P UROHIT , S., P RASHANTH , U. S., R AFF, E., SKOWRON , A., S UTAWIKA , L., AND VAN DER WAL, O. Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling, 2023

work page 2023
[6]

GPT-Neo: Large scale autoregressive lan- guage modeling with Mesh-Tensorflow, 2021

BLACK , S., G AO, L., W ANG , P., L EAHY, C., AND BI- DERMAN , S. GPT-Neo: Large scale autoregressive lan- guage modeling with Mesh-Tensorflow, 2021

work page 2021
[7]

What does it mean for a language model to preserve privacy? In ACM FAccT (2022)

BROWN , H., L EE, K., M IRESHGHALLAH , F., S HOKRI , R., AND TRAMÈR , F. What does it mean for a language model to preserve privacy? In ACM FAccT (2022)

work page 2022
[8]

B., M ANN , B., R YDER , N., S UBBIAH , M., K APLAN , J., D HARIWAL , P., N EELAKANTAN , A., SHYAM, P., ET AL

BROWN , T. B., M ANN , B., R YDER , N., S UBBIAH , M., K APLAN , J., D HARIWAL , P., N EELAKANTAN , A., SHYAM, P., ET AL . Language models are few-shot learners. In NeurIPS (2020)

work page 2020
[9]

Membership inference attacks from first principles

CARLINI , N., C HIEN , S., N ASR , M., S ONG , S., TERZIS , A., AND TRAMER , F. Membership inference attacks from first principles. In IEEE Symposium on Security and Privacy (2022), IEEE

work page 2022
[10]

Extracting training data from diffu- sion models

CARLINI , N., H AYES, J., N ASR , M., J AGIELSKI , M., SEHWAG, V., T RAMER , F., B ALLE , B., I PPOLITO , D., AND WALLACE , E. Extracting training data from diffu- sion models. In USENIX Security Symposium (2023)

work page 2023
[11]

Quantifying memoriza- tion across neural language models

CARLINI , N., I PPOLITO , D., J AGIELSKI , M., L EE, K., TRAMER , F., AND ZHANG , C. Quantifying memoriza- tion across neural language models. In ICLR (2023)

work page 2023
[12]

The secret sharer: Evaluating and testing un- intended memorization in neural networks

CARLINI , N., L IU, C., E RLINGSSON , Ú., KOS, J., AND SONG , D. The secret sharer: Evaluating and testing un- intended memorization in neural networks. In USENIX Security Symposium (2019)

work page 2019
[13]

A., JAGIELSKI , M., G AO, I., A WADALLA , A., K OH, P

CARLINI , N., N ASR , M., C HOQUETTE -CHOO , C. A., JAGIELSKI , M., G AO, I., A WADALLA , A., K OH, P. W., IPPOLITO , D., L EE, K., T RAMER , F., ET AL . Are aligned neural networks adversarially aligned? arXiv preprint arXiv:2306.15447 (2023)

work page arXiv 2023
[14]

Ex- tracting training data from large language models

CARLINI , N., T RAMER , F., W ALLACE , E., J AGIEL - SKI , M., H ERBERT -VOSS , A., L EE, K., R OBERTS , A., BROWN , T., S ONG , D., E RLINGSSON , U., ET AL . Ex- tracting training data from large language models. In USENIX Security Symposium (2021)

work page 2021
[15]

Nonparametric estimation of the number of classes in a population

CHAO, A. Nonparametric estimation of the number of classes in a population. Scandinavian Journal of statistics (1984), 265–270

work page 1984
[16]

A., AND CHAO, A

CHIU , C.-H., W ANG , Y.-T., WALTHER , B. A., AND CHAO, A. An improved nonparametric lower bound of species richness via a modified good–turing frequency formula. Biometrics 70, 3 (2014), 671–682

work page 2014
[17]

A., T RAMER , F., C ARLINI , N., AND PAPERNOT , N

CHOQUETTE -CHOO , C. A., T RAMER , F., C ARLINI , N., AND PAPERNOT , N. Label-only membership infer- ence attacks. In International conference on machine learning (2021), PMLR, pp. 1964–1974

work page 2021
[18]

F., L EIKE , J., B ROWN , T., M ARTIC , M., L EGG , S., AND AMODEI , D

C HRISTIANO , P. F., L EIKE , J., B ROWN , T., M ARTIC , M., L EGG , S., AND AMODEI , D. Deep reinforcement learning from human preferences. NeurIPS (2017)

work page 2017
[19]

RedPajama: An open source recipe to reproduce LLaMA training dataset, 2023

COMPUTER , T. RedPajama: An open source recipe to reproduce LLaMA training dataset, 2023. 16

work page 2023
[20]

Releasing 3B and 7B RedPajama- INCITE family of models including base, instruction- tuned & chat models, 2023

COMPUTER , T. Releasing 3B and 7B RedPajama- INCITE family of models including base, instruction- tuned & chat models, 2023

work page 2023
[21]

Model inversion attacks that exploit confidence informa- tion and basic countermeasures

FREDRIKSON , M., J HA, S., AND RISTENPART , T. Model inversion attacks that exploit confidence informa- tion and basic countermeasures. In ACM Conference on Computer and Communications Security (CCS) (2015)

work page 2015
[22]

A., AND SAMPSON , G

GALE , W. A., AND SAMPSON , G. Good-Turing fre- quency estimation without tears. Journal of quantitative linguistics 2, 3 (1995), 217–237

work page 1995
[23]

The Pile: An 800GB Dataset of Diverse Text for Language Modeling

GAO, L., B IDERMAN , S., B LACK , S., G OLDING , L., HOPPE , T., F OSTER , C., P HANG , J., H E, H., T HITE , A., N ABESHIMA , N., ET AL . The Pile: An 800GB dataset of diverse text for language modeling. arXiv preprint arXiv:2101.00027 (2020)

work page internal anchor Pith review Pith/arXiv arXiv 2020
[24]

GOOD , I. J. The population frequencies of species and the estimation of population parameters. Biometrika 40, 3-4 (1953), 237–264

work page 1953
[25]

HOFFMANN , J., B ORGEAUD , S., M ENSCH , A., BUCHATSKAYA , E., C AI, T., R UTHERFORD , E., CASAS , D. D. L., H ENDRICKS , L. A., W ELBL , J., CLARK , A., ET AL . Training compute-optimal large language models. In NeurIPS (2022)

work page 2022
[26]

A., W ELBL , J., C LARK , A., ET AL

HOFFMANN , J., B ORGEAUD , S., M ENSCH , A., BUCHATSKAYA , E., C AI, T., R UTHERFORD , E., DE LAS CASAS , D., H ENDRICKS , L. A., W ELBL , J., C LARK , A., ET AL . An empirical analysis of compute-optimal large language model training. Advances in Neural Information Processing Systems 35 (2022), 30016–30030

work page 2022
[27]

Training data extraction from pre-trained language models: A survey, 2023

ISHIHARA , S. Training data extraction from pre-trained language models: A survey, 2023

work page 2023
[28]

Q., S ABLAYROLLES , A., M ENSCH , A., BAMFORD , C., C HAPLOT , D

JIANG , A. Q., S ABLAYROLLES , A., M ENSCH , A., BAMFORD , C., C HAPLOT , D. S., DE LAS CASAS , D., B RESSAND , F., L ENGYEL , G., L AMPLE , G., SAULNIER , L., L AVAUD, L. R., L ACHAUX , M.-A., STOCK , P., S CAO, T. L., L AVRIL , T., W ANG , T., LACROIX , T., AND SAYED, W. E. Mistral 7b, 2023

work page 2023
[29]

Dedu- plicating training data mitigates privacy risks in lan- guage models

KANDPAL , N., W ALLACE , E., AND RAFFEL , C. Dedu- plicating training data mitigates privacy risks in lan- guage models. ICML (2022)

work page 2022
[30]

A., L EE, K., X IN, D., K USUPATI , A., S TELLA , R., B APNA , A., ET AL

KUDUGUNTA , S., C ASWELL , I., Z HANG , B., G AR- CIA , X., C HOQUETTE -CHOO , C. A., L EE, K., X IN, D., K USUPATI , A., S TELLA , R., B APNA , A., ET AL . Madlad-400: A multilingual and document-level large audited dataset. arXiv preprint arXiv:2309.04662 (2023)

work page arXiv 2023
[31]

F., AND GRIMMELMANN , J

LEE, K., C OOPER , A. F., AND GRIMMELMANN , J. Talkin’ ’Bout AI Generation: Copyright and the Generative-AI Supply Chain, 2023

work page 2023
[32]

F., G RIMMELMANN , J., AND IPPOLITO , D

LEE, K., C OOPER , A. F., G RIMMELMANN , J., AND IPPOLITO , D. AI and Law: The Next Generation, 2023

work page 2023
[33]

Deduplicating training data makes language models bet- ter

LEE, K., I PPOLITO , D., N YSTROM , A., Z HANG , C., ECK, D., C ALLISON -BURCH , C., AND CARLINI , N. Deduplicating training data makes language models bet- ter. In ACL (2022)

work page 2022
[34]

M., B ARAK , B., S CAO, T

MUENNIGHOFF , N., R USH , A. M., B ARAK , B., S CAO, T. L., P IKTUS , A., T AZI , N., P YYSALO , S., W OLF, T., AND RAFFEL , C. Scaling data-constrained language models. arXiv preprint arXiv:2305.16264 (2023)

work page arXiv 2023
[35]

ChatGPT: Optimizing Language Models for Dialogue, 2022

OPENAI. ChatGPT: Optimizing Language Models for Dialogue, 2022

work page 2022
[36]

Custom instructions for ChatGPT, 2023

O PENAI. Custom instructions for ChatGPT, 2023

work page 2023
[37]

GPT-4 System Card

O PENAI. GPT-4 System Card. Tech. rep., Mar. 2023

work page 2023
[38]

GPT-4 Technical Report

OPENAI. GPT-4 technical report. arXiv preprint arXiv:2303.08774 (2023)

work page internal anchor Pith review Pith/arXiv arXiv 2023
[39]

Training lan- guage models to follow instructions with human feed- back

OUYANG , L., W U, J., J IANG , X., A LMEIDA , D., WAINWRIGHT , C., M ISHKIN , P., Z HANG , C., A GAR - WAL, S., S LAMA , K., R AY, A., ET AL . Training lan- guage models to follow instructions with human feed- back. NeurIPS (2022)

work page 2022
[40]

The Refined- Web Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only, 2023

PENEDO , G., M ALARTIC , Q., H ESSLOW , D., C OJO - CARU , R., C APPELLI , A., A LOBEIDLI , H., P ANNIER , B., A LMAZROUEI , E., AND LAUNAY, J. The Refined- Web Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only, 2023

work page 2023
[41]

Vulnerability disclosure pol- icy

PROJECT ZERO. Vulnerability disclosure pol- icy. https://googleprojectzero.blogspot.com/p/ vulnerability-disclosure-policy.html, 2021

work page 2021
[42]

Language Models are Unsupervised Multitask Learners

RADFORD , A., W U, J., C HILD , R., L UAN, D., AMODEI , D., AND SUTSKEVER , I. Language Models are Unsupervised Multitask Learners. Tech. rep., OpenAI, 2019

work page 2019
[43]

RAFFEL , C., S HAZEER , N., R OBERTS , A., L EE, K., NARANG , S., M ATENA , M., Z HOU , Y., L I, W., AND LIU, P. J. Exploring the limits of transfer learning with a unified text-to-text transformer. JMLR (2020)

work page 2020
[44]

H., S UTAWIKA , L., A LYAFEAI , Z., C HAFFIN , A., S TIEGLER , A., S CAO, T

SANH , V., W EBSON , A., R AFFEL , C., B ACH, S. H., S UTAWIKA , L., A LYAFEAI , Z., C HAFFIN , A., S TIEGLER , A., S CAO, T. L., R AJA, A., ET AL . Multitask prompted training enables zero-shot task generalization. In ICLR (2021). 17

work page 2021
[45]

Membership inference attacks against machine learning models

SHOKRI , R., S TRONATI , M., S ONG , C., AND SHMATIKOV, V. Membership inference attacks against machine learning models. In IEEE Symposium on Security and Privacy (2017)

work page 2017
[46]

AI2 Dolma: 3 trillion token open corpus for language model pretraining, 2023

SOLDAINI , L. AI2 Dolma: 3 trillion token open corpus for language model pretraining, 2023

work page 2023
[47]

Diffusion art or digital forgery? Investigating data replication in diffusion mod- els

SOMEPALLI , G., S INGLA , V., G OLDBLUM , M., G EIP - ING , J., AND GOLDSTEIN , T. Diffusion art or digital forgery? Investigating data replication in diffusion mod- els. In CVPR (2023)

work page 2023
[48]

SOUTHWOOD , T. R. E., AND HENDERSON , P. A. Eco- logical methods. John Wiley & Sons, 2009

work page 2009
[49]

LLaMA: Open and Efficient Foundation Language Models, 2023

TOUVRON , H., L AVRIL , T., I ZACARD , G., M AR- TINET , X., L ACHAUX , M.-A., L ACROIX , T., R OZ- IÈRE , B., G OYAL, N., H AMBRO , E., A ZHAR , F., R O- DRIGUEZ , A., J OULIN , A., G RAVE, E., AND LAMPLE , G. LLaMA: Open and Efficient Foundation Language Models, 2023

work page 2023
[50]

Llama 2: Open Foundation and Fine-Tuned Chat Models

TOUVRON , H., M ARTIN , L., S TONE , K., A LBERT , P., ALMAHAIRI , A., B ABAEI , Y., BASHLYKOV , N., B A- TRA , S., BHARGAVA , P., BHOSALE , S., ET AL . LLaMA 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023)

work page internal anchor Pith review Pith/arXiv arXiv 2023
[51]

Introducing Falcon 180b

TTI. Introducing Falcon 180b

work page
[52]

Privacy risk in machine learning: Analyzing the connection to overfitting

YEOM , S., G IACOMELLI , I., F REDRIKSON , M., AND JHA, S. Privacy risk in machine learning: Analyzing the connection to overfitting. In IEEE CSF (2018)

work page 2018
[53]

Smooth nonparametric estimation of the quantile function

ZELTERMAN , D. Smooth nonparametric estimation of the quantile function. Journal of statistical planning and inference 26, 3 (1990), 339–352

work page 1990
[54]

V., M IHAYLOV, T., O TT, M., S HLEIFER , S., SHUSTER , K., S IMIG , D., K OURA , P

Z HANG , S., R OLLER , S., G OYAL, N., A RTETXE , M., CHEN , M., C HEN , S., D EWAN, C., D IAB , M., L I, X., LIN, X. V., M IHAYLOV, T., O TT, M., S HLEIFER , S., SHUSTER , K., S IMIG , D., K OURA , P. S., S RIDHAR , A., W ANG , T., AND ZETTLEMOYER , L. Opt: Open pre-trained transformer language models, 2022

work page 2022
[55]

Github Copilot research recitation, 2021

ZIEGLER , A. Github Copilot research recitation, 2021

work page 2021
[56]

Universal and Transferable Adversarial Attacks on Aligned Language Models

ZOU, A., W ANG , Z., K OLTER , J. Z., AND FREDRIK - SON , M. Universal and transferable adversarial at- tacks on aligned language models. arXiv preprint arXiv:2307.15043 (2023). A Suffix Arrays A suffix of length k of a string xxx are the last k characters (or, tokens) of this string, i.e,. xxx[−k:]. If we want to know: “was 0 100 200 300 length of k-gram...

work page internal anchor Pith review Pith/arXiv arXiv 2023
[57]

wholesale clients

classifier.fit(X_train, y_train) # Predicting the Test set results y_pred = classifier.predict(X_test) # Making the Confusion Matrix from sklearn.metrics import confusion_matrix cm = confusion_matrix(y_test, y_pred) # Visualising the Training set results from matplotlib.colors import ListedColormap X_set, y_set = X_train, y_train X1, X2 = np.meshgrid(np.a...

work page 2008
[58]

The first of her class, she was joined by sister ships MS Allure of the Seas

At the time of construction, Oasis of the Seas set a new capacity record of carrying over 6,000 passengers. The first of her class, she was joined by sister ships MS Allure of the Seas ... Grandeur of the Seas cruise ship photos, ship schedule and itineraries, special offers, picture of staterooms and discount cruises Our final day on board the Navigator ...

work page 2018
[59]

Prior to his governorship, Cooper had served as the elected Attorney General of

is an American politician and attorney serving as the 75th and current Governor of North Carolina since 2017. Prior to his governorship, Cooper had served as the elected Attorney General of ... Local News The Lorrha Notes are compiled weekly by Rose Mannion who is the local correspondant for a number of regional papers. Contact Rose at or or by emailing ....

work page 2017
[60]

Gravitation

But Shane Guidry, a little-known ... This list of Duke University people includes alumni, faculty, presidents, and major philanthropists of Duke University, which includes three undergraduate and ten graduate schools. Florida Polling. Contact: Doug Kaplan, Executive Summary Gravis Marketing, a nonpartisan research firm, conducted a random survey ... Each ...

work page 1940
[61]

-Pocket Gamer A lot of tanks, a lot of people and a lot of fun

The very best multiplayer youll find for your mobile. -Pocket Gamer A lot of tanks, a lot of people and a lot of fun. -IGN World of Tanks Blitz is a free-to-play mobile MMO action game brought to you by Wargaming, the award-winning online game developer and publisher of World of. Jumpstart your grads next adventure what 1475 cruisers had to say about thei...

work page 2007
[62]

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

was an American actor and one of silent films earliest superstars. The Runner-Up Takes It All trope as used in popular culture. When the person who comes second or worse in a Reality Show gets more out of it than the winner ... Part of the Route 67 series In yesterday’s post I included a quote from Ben Hogan that said: The main thing for the novice or the...

work page 1957
[63]

I worked for arise for a while and I have to agree it is a scam

The obverse or heads side was designed by Victor David Brenner, as was the original reverse. I worked for arise for a while and I have to agree it is a scam. first to begin with they cheated me out of pay. Do you know how hard it is to dispute pay by email or chat in a virtual environment. UPDATED 24 January 2012 What We Need for 21st Century Combat. quot...

work page 2012
[64]

personalised service

The obverse or heads side was designed by Victor David Brenner, as was the original reverse. I worked for arise for a while and I have to agree it is a scam 54 Verbatim Extracted Text 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 secur...

work page 2017