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REVIEW 1 major objections 2 minor 60 cited by

RLHF-trained language models become progressively harder to red-team into harmful outputs as they scale up in size, while other training approaches show no such improvement.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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T0 review · grok-4.3

2026-05-12 01:32 UTC pith:RELJI4P3

load-bearing objection RLHF models get harder to red team with scale while the other types stay flat, plus a released dataset of 39k attacks. the 1 major comments →

arxiv 2209.07858 v2 pith:RELJI4P3 submitted 2022-08-23 cs.CL cs.AIcs.CY

Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned

Deep Ganguli , Liane Lovitt , Jackson Kernion , Amanda Askell , Yuntao Bai , Saurav Kadavath , Ben Mann , Ethan Perez
show 28 more authors
Nicholas Schiefer Kamal Ndousse Andy Jones Sam Bowman Anna Chen Tom Conerly Nova DasSarma Dawn Drain Nelson Elhage Sheer El-Showk Stanislav Fort Zac Hatfield-Dodds Tom Henighan Danny Hernandez Tristan Hume Josh Jacobson Scott Johnston Shauna Kravec Catherine Olsson Sam Ringer Eli Tran-Johnson Dario Amodei Tom Brown Nicholas Joseph Sam McCandlish Chris Olah Jared Kaplan Jack Clark
This is my paper
classification cs.CL cs.AIcs.CY
keywords red teaminglanguage modelsRLHFscaling behaviorsharmful outputssafety evaluationdataset release
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The paper tests whether red teaming—deliberately prompting models to produce harmful responses—reveals different patterns of vulnerability depending on model size and training method. It compares plain language models, prompted helpful-honest-harmless models, rejection-sampling models, and RLHF models across three sizes from 2.7B to 52B parameters. The central result is that only the RLHF models grow harder to attack with scale; success rates for the other three categories remain roughly flat. The authors also release the full set of 38,961 attacks they collected and lay out their exact procedures so others can replicate or improve on the work.

Core claim

Across the tested model sizes and types, RLHF models show a clear increase in resistance to red team attacks as parameter count grows, whereas plain LMs, prompted LMs, and rejection-sampling LMs exhibit flat trends in attack success rate with scale. The work further catalogs a wide range of elicited harms, from overt offensive language to subtler non-violent unethical content, and supplies the complete attack dataset together with detailed methodology for community use.

What carries the argument

Comparative red-teaming success rate measured across four model training regimes (plain LM, prompted HH, rejection sampling, RLHF) at three parameter scales, with the RLHF regime as the variable that produces the observed scaling improvement in resistance.

Load-bearing premise

The particular red-teaming instructions, prompts, and attack strategies used in the study are comprehensive enough to surface most or all of the harmful behaviors these models can exhibit.

What would settle it

A follow-up experiment that applies the same or closely matched attack distribution to a substantially larger RLHF model (for example 100B+ parameters) and measures an attack success rate that does not continue to decline, or that rises, would falsify the reported scaling trend for RLHF.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • Larger RLHF models will likely need more advanced or automated red-teaming methods to continue uncovering residual harms.
  • The released attack dataset supplies a public benchmark that future safety methods can be measured against.
  • Training regimes other than RLHF do not appear to confer the same scaling advantage in resistance to attack.
  • Transparency in red-teaming procedures enables shared standards for evaluating model safety across labs.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the scaling pattern holds, RLHF-style training may provide a practical route for safety to improve alongside raw capability at larger scales.
  • The flat trends for non-RLHF models suggest that prompt engineering or rejection sampling alone are unlikely to close the safety gap as models grow.
  • The methods could be extended to test whether similar scaling resistance appears in multimodal or agentic systems trained with comparable feedback.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

1 major / 2 minor

Summary. The paper describes early efforts to red team language models to discover, measure, and reduce harmful outputs. It makes three contributions: (1) an investigation of scaling behaviors for red teaming success across three model sizes (2.7B, 13B, 52B) and four model types (plain LM, prompted helpful/honest/harmless, rejection sampling, and RLHF), finding that RLHF models become increasingly difficult to red team with scale while the other types show flat trends; (2) release of a dataset containing 38,961 red team attacks; and (3) detailed descriptions of instructions, processes, statistical methodologies, and uncertainties, along with analysis of the harmful outputs elicited (ranging from offensive language to subtle unethical behaviors).

Significance. If the scaling trends prove robust, the work supplies concrete empirical data on how alignment methods like RLHF affect vulnerability to adversarial elicitation of harms, informing safer deployment of larger models. The public release of the large attack dataset is a clear asset that enables independent verification and further research on red teaming techniques. The paper's emphasis on methodological transparency and explicit discussion of uncertainties is a positive contribution toward community standards in AI safety evaluation.

major comments (1)
  1. [Scaling behaviors] Scaling behaviors section (and abstract claim): The central result that RLHF models are increasingly difficult to red team with scale, while other model types remain flat, assumes consistent red teaming effort and strategy across conditions. The manuscript does not report per-model metrics on attack persistence (e.g., average turns per conversation, number of unique prompt variants tried, or stopping criteria) or indicate whether red teamers were blinded to model identity or type. Without such controls, lower success rates on larger RLHF models could reflect differences in human effort or adaptation rather than intrinsic scaling of refusal behavior. Although the released dataset permits post-hoc checks, the paper should include an analysis of effort-related statistics across the four model types to support the scaling interpretation.
minor comments (2)
  1. [Methods] Methods section: While the paper states it exhaustively describes statistical methodologies, adding explicit formulas or pseudocode for how red team success rates and uncertainty estimates were computed (including any adjustments for multiple comparisons across model sizes) would improve reproducibility.
  2. [Dataset] Dataset description: The release of 38,961 attacks is valuable, but the paper would benefit from additional metadata on red teamer demographics, experience levels, and any training provided, to allow readers to assess potential sources of bias in the attack distribution.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback and positive assessment of the paper's contributions to red teaming methods and the public dataset release. We address the major comment below.

read point-by-point responses

  1. Referee: [Scaling behaviors] Scaling behaviors section (and abstract claim): The central result that RLHF models are increasingly difficult to red team with scale, while other model types remain flat, assumes consistent red teaming effort and strategy across conditions. The manuscript does not report per-model metrics on attack persistence (e.g., average turns per conversation, number of unique prompt variants tried, or stopping criteria) or indicate whether red teamers were blinded to model identity or type. Without such controls, lower success rates on larger RLHF models could reflect differences in human effort or adaptation rather than intrinsic scaling of refusal behavior. Although the released dataset permits post-hoc checks, the paper should include an analysis of effort-related statistics across the four model types to support the scaling interpretation.

    Authors: We thank the referee for highlighting this important potential confound in our scaling analysis. We agree that the absence of reported effort metrics leaves room for alternative interpretations. Our red teaming protocol used identical instructions, attack strategies, and stopping criteria for all model types and sizes, as described in the methods. However, we did not report per-model statistics on conversation length or prompt variants, and red teamers were not blinded to model identity. To address this directly, we will perform a post-hoc analysis of the released dataset of 38,961 attacks to compute effort-related metrics (average turns, unique variants attempted) broken down by model type and size, and include these results in the revised manuscript. This addition will support that the RLHF scaling trend reflects model behavior rather than differences in human effort. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical scaling trends derived from direct measurements

full rationale

The paper reports empirical results from human red-teaming experiments across model scales and types, with the central claim (RLHF models become harder to red-team with scale while others show flat trends) resting on observed attack success rates in the released dataset of 38,961 attacks. No mathematical derivations, fitted parameters renamed as predictions, or self-citations are used to establish the scaling behaviors; the trends follow directly from the collected data without reduction to prior inputs or definitions. Self-citations appear only for background methods and are not load-bearing for the scaling observations. The work is self-contained against external benchmarks via the public dataset, which permits independent verification.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper is empirical and does not introduce new mathematical axioms, free parameters, or invented entities; it builds on standard practices in machine learning and AI safety evaluation.

axioms (1)
  • domain assumption Human evaluators can reliably identify harmful outputs from language models
    Underlying the red teaming and data analysis process.

pith-pipeline@v0.9.0 · 5668 in / 1224 out tokens · 76750 ms · 2026-05-12T01:32:45.315412+00:00 · methodology

0 comments
read the original abstract

We describe our early efforts to red team language models in order to simultaneously discover, measure, and attempt to reduce their potentially harmful outputs. We make three main contributions. First, we investigate scaling behaviors for red teaming across 3 model sizes (2.7B, 13B, and 52B parameters) and 4 model types: a plain language model (LM); an LM prompted to be helpful, honest, and harmless; an LM with rejection sampling; and a model trained to be helpful and harmless using reinforcement learning from human feedback (RLHF). We find that the RLHF models are increasingly difficult to red team as they scale, and we find a flat trend with scale for the other model types. Second, we release our dataset of 38,961 red team attacks for others to analyze and learn from. We provide our own analysis of the data and find a variety of harmful outputs, which range from offensive language to more subtly harmful non-violent unethical outputs. Third, we exhaustively describe our instructions, processes, statistical methodologies, and uncertainty about red teaming. We hope that this transparency accelerates our ability to work together as a community in order to develop shared norms, practices, and technical standards for how to red team language models.

discussion (0)

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Reference graph

Works this paper leans on

64 extracted references · 64 canonical work pages · cited by 147 Pith papers · 12 internal anchors

  1. [1]

    A. Abid, M. Farooqi, and J. Zou. Large language models associate Muslims with violence. Nature Machine Intelligence, 3(6):461–463, June 2021. Number: 6 Publisher: Nature Publishing Group

    work page 2021

  2. [2]

    A General Language Assistant as a Laboratory for Alignment

    A. Askell, Y . Bai, A. Chen, D. Drain, D. Ganguli, T. Henighan, A. Jones, N. Joseph, B. Mann, N. Das- Sarma, N. Elhage, Z. Hatfield-Dodds, D. Hernandez, J. Kernion, K. Ndousse, C. Olsson, D. Amodei, T. Brown, J. Clark, S. McCandlish, C. Olah, and J. Kaplan. A General Language Assistant as a Labora- tory for Alignment. arXiv:2112.00861 [cs], Dec. 2021. arXi...

    work page internal anchor Pith review Pith/arXiv arXiv 2021

  3. [3]

    S. Avin, H. Belfield, M. Brundage, G. Krueger, J. Wang, A. Weller, M. Anderljung, I. Krawczuk, D. Krueger, J. Lebensold, T. Maharaj, and N. Zilberman. Filling gaps in trustworthy development of AI. Science, Dec. 2021. Publisher: American Association for the Advancement of Science

    work page 2021

  4. [4]

    Y . Bai, A. Jones, K. Ndousse, A. Askell, A. Chen, N. DasSarma, D. Drain, S. Fort, D. Ganguli, T. Henighan, N. Joseph, S. Kadavath, J. Kernion, T. Conerly, S. El-Showk, N. Elhage, Z. Hatfield- Dodds, D. Hernandez, T. Hume, S. Johnston, S. Kravec, L. Lovitt, N. Nanda, C. Olsson, D. Amodei, T. Brown, J. Clark, S. McCandlish, C. Olah, B. Mann, and J. Kaplan. ...

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  5. [5]

    P. Barrett. Research Highlights | Who Moderates the Social Media Giants? A Call to End Outsourcing - NYU Stern

    work page

  6. [6]

    Bartolo, T

    M. Bartolo, T. Thrush, R. Jia, S. Riedel, P. Stenetorp, and D. Kiela. Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing , pages 8830–8848, 2021. arXiv:2104.08678 [cs]

    work page arXiv 2021

  7. [7]

    Evaluating the Underlying Gender Bias in Contextualized Word Embeddings

    C. Basta, M. R. Costa-jussà, and N. Casas. Evaluating the Underlying Gender Bias in Contextualized Word Embeddings. arXiv:1904.08783 [cs], Apr. 2019. arXiv: 1904.08783

    work page Pith review arXiv 1904

  8. [8]

    Bates, M

    D. Bates, M. Mächler, B. Bolker, and S. Walker. Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67(1):1–48, 2015

    work page 2015

  9. [9]

    E. M. Bender, T. Gebru, A. McMillan-Major, and S. Shmitchell. On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? InProceedings of the 2021 ACM Conference on Fairness, Account- ability, and Transparency, FAccT ’21, pages 610–623, New York, NY , USA, Mar. 2021. Association for Computing Machinery

    work page 2021

  10. [10]

    On the Opportunities and Risks of Foundation Models

    R. Bommasani, D. A. Hudson, E. Adeli, R. Altman, S. Arora, S. von Arx, M. S. Bernstein, J. Bohg, A. Bosselut, E. Brunskill, E. Brynjolfsson, S. Buch, D. Card, R. Castellon, N. Chatterji, A. Chen, K. Creel, J. Q. Davis, D. Demszky, C. Donahue, M. Doumbouya, E. Durmus, S. Ermon, J. Etchemendy, K. Ethayarajh, L. Fei-Fei, C. Finn, T. Gale, L. Gillespie, K. Go...

    work page internal anchor Pith review Pith/arXiv arXiv 2021

  11. [11]

    TowardtrustworthyAI development: Mechanisms for supporting verifiable claims,

    M. Brundage, S. Avin, J. Wang, H. Belfield, G. Krueger, G. Hadfield, H. Khlaaf, J. Yang, H. Toner, R. Fong, T. Maharaj, P. W. Koh, S. Hooker, J. Leung, A. Trask, E. Bluemke, J. Lebensold, C. O’Keefe, M. Koren, T. Ryffel, J. B. Rubinovitz, T. Besiroglu, F. Carugati, J. Clark, P. Eckersley, S. de Haas, M. Johnson, B. Laurie, A. Ingerman, I. Krawczuk, A. Askel...

    work page arXiv 2004

  12. [12]

    Buchanan, A

    B. Buchanan, A. Lohn, M. Musser, and K. Sedova. Truth, Lies, and Automation, May 2021

    work page 2021

  13. [13]

    Carlini, F

    N. Carlini, F. Tramer, E. Wallace, M. Jagielski, A. Herbert-V oss, K. Lee, A. Roberts, T. Brown, D. Song, U. Erlingsson, A. Oprea, and C. Raffel. Extracting Training Data from Large Language Models. arXiv:2012.07805 [cs], June 2021. arXiv: 2012.07805

    work page arXiv 2012

  14. [14]

    P. F. Christiano, J. Leike, T. Brown, M. Martic, S. Legg, and D. Amodei. Deep Reinforcement Learning from Human Preferences. In I. Guyon, U. V . Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vish- wanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems , volume 30. Curran Associates, Inc., 2017

    work page 2017

  15. [15]

    B. Dang, M. J. Riedl, and M. Lease. But Who Protects the Moderators? The Case of Crowdsourced Image Moderation, Jan. 2020. arXiv:1804.10999 [cs]

    work page arXiv 2020

  16. [16]

    A. Das, B. Dang, and M. Lease. Fast, Accurate, and Healthier: Interactive Blurring Helps Moderators Reduce Exposure to Harmful Content. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 8(1):33–42, Oct. 2020

    work page 2020

  17. [17]

    Diener, D

    E. Diener, D. Wirtz, W. Tov, C. Kim-Prieto, D.-w. Choi, S. Oishi, and R. Biswas-Diener. New Well- being Measures: Short Scales to Assess Flourishing and Positive and Negative Feelings. Social Indica- tors Research, 97(2):143–156, June 2010

    work page 2010

  18. [18]

    Stevie Bergman, Shannon Spruit, Dirk Hovy, Y-Lan Boureau, and Verena Rieser

    E. Dinan, G. Abercrombie, A. S. Bergman, S. Spruit, D. Hovy, Y .-L. Boureau, and V . Rieser. Antici- pating Safety Issues in E2E Conversational AI: Framework and Tooling. arXiv:2107.03451 [cs], July

    work page arXiv

  19. [19]

    Dinan, A

    E. Dinan, A. Fan, A. Williams, J. Urbanek, D. Kiela, and J. Weston. Queens are Powerful too: Mit- igating Gender Bias in Dialogue Generation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8173–8188, Online, Nov. 2020. Association for Computational Linguistics

    work page 2020

  20. [20]

    Build it break it fix it for dialogue safety: Robustness from adversarial human attack

    E. Dinan, S. Humeau, B. Chintagunta, and J. Weston. Build it Break it Fix it for Dialogue Safety: Robustness from Adversarial Human Attack, Aug. 2019. arXiv:1908.06083 [cs]

    work page arXiv 2019

  21. [21]

    Dixon, J

    L. Dixon, J. Li, J. Sorensen, N. Thain, and L. Vasserman. Measuring and Mitigating Unintended Bias in Text Classification. In Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society , AIES ’18, pages 67–73, New York, NY , USA, Dec. 2018. Association for Computing Machinery

    work page 2018

  22. [22]

    Predictability and Surprise in Large Generative Models , url =

    D. Ganguli, D. Hernandez, L. Lovitt, N. DasSarma, T. Henighan, A. Jones, N. Joseph, J. Kernion, B. Mann, A. Askell, Y . Bai, A. Chen, T. Conerly, D. Drain, N. Elhage, S. E. Showk, S. Fort, Z. Hatfield- Dodds, S. Johnston, S. Kravec, N. Nanda, K. Ndousse, C. Olsson, D. Amodei, D. Amodei, T. Brown, J. Kaplan, S. McCandlish, C. Olah, and J. Clark. Predictabil...

    work page arXiv 2022

  23. [23]

    S. Garg, V . Perot, N. Limtiaco, A. Taly, E. H. Chi, and A. Beutel. Counterfactual Fairness in Text Classification through Robustness. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, AIES ’19, pages 219–226, New York, NY , USA, Jan. 2019. Association for Computing Machinery

    work page 2019

  24. [24]

    paper”, “product

    T. Gebru, J. Morgenstern, B. Vecchione, J. W. Vaughan, H. Wallach, H. Daumé III, and K. Crawford. Datasheets for Datasets. arXiv:1803.09010 [cs], Dec. 2021. arXiv: 1803.09010

    work page arXiv 2021

  25. [25]

    RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models

    S. Gehman, S. Gururangan, M. Sap, Y . Choi, and N. A. Smith. RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models. ArXiv, abs/2009.11462, 2020

    work page internal anchor Pith review arXiv 2009

  26. [26]

    Gray and S

    M. Gray and S. Suri. Ghost Work. Mariner Books, 2019. 27

    work page 2019

  27. [27]

    E. A. Holmes, E. L. James, T. Coode-Bate, and C. Deeprose. Can Playing the Computer Game “Tetris” Reduce the Build-Up of Flashbacks for Trauma? A Proposal from Cognitive Science. PLOS ONE, 4(1):e4153, Jan. 2009. Publisher: Public Library of Science

    work page 2009

  28. [28]

    Hutchinson, V

    B. Hutchinson, V . Prabhakaran, E. Denton, K. Webster, Y . Zhong, and S. Denuyl. Social Biases in NLP Models as Barriers for Persons with Disabilities. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics , pages 5491–5501, Online, July 2020. Association for Computational Linguistics

    work page 2020

  29. [29]

    Jia and P

    R. Jia and P. Liang. Adversarial Examples for Evaluating Reading Comprehension Systems. InProceed- ings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2021–2031, Copenhagen, Denmark, Sept. 2017. Association for Computational Linguistics

    work page 2017

  30. [30]

    Jiang and M

    Y . Jiang and M. Bansal. Avoiding Reasoning Shortcuts: Adversarial Evaluation, Training, and Model Development for Multi-Hop QA. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2726–2736, Florence, Italy, July 2019. Association for Computational Linguistics

    work page 2019

  31. [31]

    Karunakaran and R

    S. Karunakaran and R. Ramakrishan. Testing Stylistic Interventions to Reduce Emotional Impact of Content Moderation Workers.Proceedings of the AAAI Conference on Human Computation and Crowd- sourcing, 7:50–58, Oct. 2019

    work page 2019

  32. [32]

    Kiela, M

    D. Kiela, M. Bartolo, Y . Nie, D. Kaushik, A. Geiger, Z. Wu, B. Vidgen, G. Prasad, A. Singh, P. Ringshia, Z. Ma, T. Thrush, S. Riedel, Z. Waseem, P. Stenetorp, R. Jia, M. Bansal, C. Potts, and A. Williams. Dynabench: Rethinking Benchmarking in NLP. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Lin...

    work page 2021

  33. [33]

    Measuring Bias in Contextualized Word Representations

    K. Kurita, N. Vyas, A. Pareek, A. W. Black, and Y . Tsvetkov. Measuring Bias in Contextualized Word Representations. arXiv:1906.07337 [cs], June 2019. arXiv: 1906.07337

    work page Pith review arXiv 1906

  34. [34]

    P. P. Liang, C. Wu, L.-P. Morency, and R. Salakhutdinov. Towards Understanding and Mitigating Social Biases in Language Models. arXiv:2106.13219 [cs], June 2021. arXiv: 2106.13219

    work page arXiv 2021

  35. [35]

    S. Lin, J. Hilton, and O. Evans. TruthfulQA: Measuring How Models Mimic Human Falsehoods. arXiv:2109.07958 [cs], Sept. 2021. arXiv: 2109.07958

    work page internal anchor Pith review Pith/arXiv arXiv 2021

  36. [36]

    A. Liu, M. Sap, X. Lu, S. Swayamdipta, C. Bhagavatula, N. A. Smith, and Y . Choi. DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts. arXiv:2105.03023 [cs], June 2021. arXiv: 2105.03023

    work page arXiv 2021

  37. [37]

    The radicalization risks of gpt-3 and advanced neural language models

    K. McGuffie and A. Newhouse. The Radicalization Risks of GPT-3 and Advanced Neural Language Models. arXiv:2009.06807 [cs], Sept. 2020. arXiv: 2009.06807

    work page arXiv 2009

  38. [38]

    UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

    L. McInnes, J. Healy, and J. Melville. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction, Sept. 2020. arXiv:1802.03426 [cs, stat]

    work page internal anchor Pith review Pith/arXiv arXiv 2020

  39. [39]

    Mishkin, L

    P. Mishkin, L. Ahmad, M. Brundage, G. Krueger, and G. Sastry. DALL·E 2 Preview - Risks and Limitations, 2022

    work page 2022

  40. [40]

    Y . Nie, A. Williams, E. Dinan, M. Bansal, J. Weston, and D. Kiela. Adversarial NLI: A New Benchmark for Natural Language Understanding. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics , pages 4885–4901, Online, July 2020. Association for Computational Linguistics

    work page 2020

  41. [41]

    Ouyang, J

    L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. L. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, J. Schulman, J. Hilton, F. Kelton, L. Miller, M. Simens, A. Askell, P. Welinder, P. Christiano, J. Leike, and R. Lowe. Training language models to follow instructions with human feedback, Mar

    work page

  42. [42]

    arXiv:2203.02155 [cs]

    work page internal anchor Pith review Pith/arXiv arXiv

  43. [43]

    Red Teaming Language Models with Language Models

    E. Perez, S. Huang, F. Song, T. Cai, R. Ring, J. Aslanides, A. Glaese, N. McAleese, and G. Irving. Red Teaming Language Models with Language Models. arXiv:2202.03286 [cs], Feb. 2022. arXiv: 2202.03286

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  44. [44]

    J. W. Rae, S. Borgeaud, T. Cai, K. Millican, J. Hoffmann, F. Song, J. Aslanides, S. Henderson, R. Ring, S. Young, E. Rutherford, T. Hennigan, J. Menick, A. Cassirer, R. Powell, G. v. d. Driessche, L. A. Hen- dricks, M. Rauh, P.-S. Huang, A. Glaese, J. Welbl, S. Dathathri, S. Huang, J. Uesato, J. Mellor, I. Hig- gins, A. Creswell, N. McAleese, A. Wu, E. El...

    work page internal anchor Pith review Pith/arXiv arXiv 2021

  45. [45]

    Hierarchical Text-Conditional Image Generation with CLIP Latents

    A. Ramesh, P. Dhariwal, A. Nichol, C. Chu, and M. Chen. Hierarchical Text-Conditional Image Gen- eration with CLIP Latents, Apr. 2022. arXiv:2204.06125 [cs]

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  46. [46]

    M. T. Ribeiro, T. Wu, C. Guestrin, and S. Singh. Beyond Accuracy: Behavioral Testing of NLP Models with CheckList. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4902–4912, Online, July 2020. Association for Computational Linguistics

    work page 2020

  47. [47]

    Röttger, B

    P. Röttger, B. Vidgen, D. Nguyen, Z. Waseem, H. Margetts, and J. Pierrehumbert. HateCheck: Func- tional Tests for Hate Speech Detection Models. In Proceedings of the 59th Annual Meeting of the Asso- ciation for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 41–58, Online,...

    work page 2021

  48. [48]

    M. Sap, S. Gabriel, L. Qin, D. Jurafsky, N. A. Smith, and Y . Choi. Social Bias Frames: Reasoning about Social and Power Implications of Language. arXiv:1911.03891 [cs], Apr. 2020. arXiv: 1911.03891

    work page arXiv 1911

  49. [49]

    CoRR , volume =

    I. Solaiman and C. Dennison. Process for Adapting Language Models to Society (PALMS) with Values- Targeted Datasets. arXiv:2106.10328 [cs], Nov. 2021. arXiv: 2106.10328

    work page arXiv 2021

  50. [50]

    Steiger, T

    M. Steiger, T. J. Bharucha, S. Venkatagiri, M. J. Riedl, and M. Lease. The Psychological Well-Being of Content Moderators: The Emotional Labor of Commercial Moderation and Avenues for Improving Support. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems , CHI ’21, pages 1–14, New York, NY , USA, May 2021. Association for Compu...

    work page 2021

  51. [51]

    Intriguing properties of neural networks

    C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. Intriguing properties of neural networks, Feb. 2014. arXiv:1312.6199 [cs]

    work page internal anchor Pith review Pith/arXiv arXiv 2014

  52. [52]

    Understanding the capabilities, limitations, and societal impact of large language models.arXiv preprint arXiv:2102.02503,

    A. Tamkin, M. Brundage, J. Clark, and D. Ganguli. Understanding the Capabilities, Limitations, and Societal Impact of Large Language Models. arXiv:2102.02503 [cs], Feb. 2021. arXiv: 2102.02503

    work page arXiv 2021

  53. [53]

    E. R. Thompson. Development and Validation of an Internationally Reliable Short-Form of the Positive and Negative Affect Schedule (PANAS). Journal of Cross-Cultural Psychology, 38(2):227–242, Mar

    work page

  54. [54]

    Publisher: SAGE Publications Inc

    work page

  55. [55]

    LaMDA: Language Models for Dialog Applications

    R. Thoppilan, D. De Freitas, J. Hall, N. Shazeer, A. Kulshreshtha, H.-T. Cheng, A. Jin, T. Bos, L. Baker, Y . Du, Y . Li, H. Lee, H. S. Zheng, A. Ghafouri, M. Menegali, Y . Huang, M. Krikun, D. Lepikhin, J. Qin, D. Chen, Y . Xu, Z. Chen, A. Roberts, M. Bosma, Y . Zhou, C.-C. Chang, I. Krivokon, W. Rusch, M. Pickett, K. Meier-Hellstern, M. R. Morris, T. Do...

    work page Pith review arXiv 2022

  56. [56]

    C. US. U.S. Census Bureau QuickFacts: United States, July 2021

    work page 2021

  57. [57]

    Wallace, A

    E. Wallace, A. Williams, R. Jia, and D. Kiela. Analyzing Dynamic Adversarial Training Data in the Limit, Oct. 2021. arXiv:2110.08514 [cs]

    work page arXiv 2021

  58. [58]

    Watson, L

    D. Watson, L. A. Clark, and A. Tellegen. Development and validation of brief measures of positive and negative affect: the PANAS scales. Journal of Personality and Social Psychology , 54(6):1063–1070, June 1988

    work page 1988

  59. [59]

    Ethical and social risks of harm from Language Models

    L. Weidinger, J. Mellor, M. Rauh, C. Griffin, J. Uesato, P.-S. Huang, M. Cheng, M. Glaese, B. Balle, A. Kasirzadeh, Z. Kenton, S. Brown, W. Hawkins, T. Stepleton, C. Biles, A. Birhane, J. Haas, L. Rimell, L. A. Hendricks, W. Isaac, S. Legassick, G. Irving, and I. Gabriel. Ethical and social risks of harm from Language Models. arXiv:2112.04359 [cs], Dec. 20...

    work page internal anchor Pith review Pith/arXiv arXiv 2021

  60. [60]

    Welbl, A

    J. Welbl, A. Glaese, J. Uesato, S. Dathathri, J. Mellor, L. A. Hendricks, K. Anderson, P. Kohli, B. Cop- pin, and P.-S. Huang. Challenges in Detoxifying Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2021 , pages 2447–2469, Punta Cana, Dominican Republic, Nov

    work page 2021

  61. [61]

    Association for Computational Linguistics

    work page

  62. [62]

    H. Xu, Y . Ma, H. Liu, D. Deb, H. Liu, J. Tang, and A. K. Jain. Adversarial Attacks and Defenses in Images, Graphs and Text: A Review, Oct. 2019. arXiv:1909.08072 [cs, stat]. 29

    work page arXiv 2019

  63. [63]

    J. Xu, D. Ju, M. Li, Y .-L. Boureau, J. Weston, and E. Dinan. Bot-Adversarial Dialogue for Safe Conversational Agents. In K. Toutanova, A. Rumshisky, L. Zettlemoyer, D. Hakkani-Tür, I. Beltagy, S. Bethard, R. Cotterell, T. Chakraborty, and Y . Zhou, editors, Proceedings of the 2021 Conference of the North American Chapter of the Association for Computatio...

    work page 2021

  64. [64]

    D. M. Ziegler, S. Nix, L. Chan, T. Bauman, P. Schmidt-Nielsen, T. Lin, A. Scherlis, N. Nabeshima, B. Weinstein-Raun, D. de Haas, B. Shlegeris, and N. Thomas. Adversarial Training for High-Stakes Reliability, May 2022. arXiv:2205.01663 [cs]. 30

    work page arXiv 2022