🔗 Who cites «Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasets»

100 papers of our library cite it (of the 149,439 whose bibliographies we read) · 45 with a page here, the most cited first · ← back to «Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasets»

1. GPT-4 Technical ReportarXiv:2303.08774OpenAI, Josh Achiam, Steven Adler, et al.2023 · 13,036 citations
2. Training language models to follow instructions with human feedbackarXiv:2203.02155Long Ouyang, Jeff Wu, Xu Jiang, et al.2022 · 10,224 citations
3. On the Opportunities and Risks of Foundation ModelsarXiv:2108.07258Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.2021 · 3,160 citations
4. TruthfulQA: Measuring How Models Mimic Human FalsehoodsAnnual Meeting of the Association for Computational Linguistics (ACL)Stephanie Lin, Jacob Hilton, Owain Evans2022 · 2,088 citations
5. Finetuned Language Models Are Zero-Shot LearnersarXiv:2109.01652Jason Wei, Maarten Bosma, Vincent Y. Zhao, et al.2021 · 1,954 citations
6. LaMDA: Language Models for Dialog ApplicationsarXiv:2201.08239Romal Thoppilan, Daniel De Freitas, Jamie Hall, et al.2022 · 1,344 citations
7. The Rise and Potential of Large Language Model Based Agents: A SurveyScience China Information SciencesZhiheng Xi, Wenxiang Chen, Xin Guo, et al.2023 · 1,272 citations
8. Bias and Fairness in Large Language Models: A SurveyComputational LinguisticsIsabel O. Gallegos, Ryan A. Rossi, Joe Barrow, et al.2023 · 1,082 citations
9. Constitutional AI: Harmlessness from AI FeedbackarXiv:2212.08073Yuntao Bai, Saurav Kadavath, Sandipan Kundu, et al.2022 · 1,028 citations
10. Can Large Language Models Transform Computational Social Science?Computational LinguisticsCaleb Ziems, William Held, Omar Shaikh, et al.2023 · 790 citations
11. Red Teaming Language Models with Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Ethan Perez, Saffron Huang, Francis Song, et al.2022 · 700 citations
12. Jailbroken: How Does LLM Safety Training Fail?Conference on Neural Information Processing Systems (NeurIPS)Alexander Wei, Nika Haghtalab, Jacob Steinhardt2023 · 688 citations
13. Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons LearnedarXiv:2209.07858Deep Ganguli, Liane Lovitt, Jackson Kernion, et al.2022 · 485 citations
14. Ethical and social risks of harm from Language ModelsarXiv:2112.04359Laura Weidinger, John Mellor, Maribeth Rauh, et al.2021 · 456 citations
15. Text and Code Embeddings by Contrastive Pre-TrainingarXiv:2201.10005Arvind Neelakantan, Tao Xu, Raul Puri, et al.2022 · 323 citations
16. Challenges and Applications of Large Language ModelsarXiv:2307.10169Jean Kaddour, Joshua Harris, Maximilian Mozes, et al.2023 · 301 citations
17. Challenges and Strategies in Cross-Cultural NLPAnnual Meeting of the Association for Computational Linguistics (ACL)Daniel Hershcovich, Stella Frank, Heather Lent, et al.2022 · 292 citations
18. Towards Measuring the Representation of Subjective Global Opinions in Language ModelsarXiv:2306.16388Esin Durmus, Karina Nguyen, Thomas I. Liao, et al.2023 · 270 citations
19. Predictability and Surprise in Large Generative ModelsACM Conference on Fairness, Accountability, and Transparency (FAccT)Deep Ganguli, Danny Hernandez, Liane Lovitt, et al.2022 · 268 citations
20. DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT ModelsConference on Neural Information Processing Systems (NeurIPS)Boxin Wang, Weixin Chen, Hengzhi Pei, et al.2023 · 259 citations
21. Red teaming ChatGPT via Jailbreaking: Bias, Robustness, Reliability and ToxicityarXiv:2301.12867Terry Yue Zhuo, Yujin Huang, Chunyang Chen, et al.2023 · 249 citations
22. Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human SupervisionConference on Neural Information Processing Systems (NeurIPS)Zhiqing Sun, Yikang Shen, Qinhong Zhou, et al.2023 · 236 citations
23. Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' AlignmentarXiv:2308.05374Yang Liu; Yuanshun Yao; Jean-Francois Ton; Xiaoying Zhang; Ruocheng Guo; Hao Cheng; Yegor Klochkov; Muhammad Faaiz Taufiq;, Hang Li2023 · 223 citations
24. Prompting GPT-3 To Be ReliablearXiv:2210.09150Chenglei Si, Zhe Gan, Zhengyuan Yang, et al.2022 · 210 citations
25. On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot ReasoningAnnual Meeting of the Association for Computational Linguistics (ACL)Omar Shaikh, Hongxin Zhang, William Held, et al.2023 · 192 citations
26. GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak PromptsarXiv:2309.10253Jiahao Yu, Xingwei Lin, Zheng Yu, et al.2023 · 169 citations
27. GPT-4 Is Too Smart To Be Safe: Stealthy Chat with LLMs via CipherarXiv:2308.06463Youliang Yuan, Wenxiang Jiao, Wenxuan Wang, et al.2023 · 168 citations
28. Probing Pre-Trained Language Models for Cross-Cultural Differences in ValuesWorkshop on Cross-Cultural Considerations in NLPArnav Arora, Lucie-aimée Kaffee, Isabelle Augenstein2023 · 156 citations
29. A Roadmap to Pluralistic AlignmentarXiv:2402.05070Taylor Sorensen, Jared Moore, Jillian Fisher, et al.2024 · 143 citations
30. Self-Alignment with Instruction BacktranslationarXiv:2308.06259Xian Li, Ping Yu, Chunting Zhou, et al.2023 · 139 citations
31. Evaluating the Moral Beliefs Encoded in LLMsConference on Neural Information Processing Systems (NeurIPS)Nino Scherrer, Claudia Shi, Amir Feder, et al.2023 · 129 citations
32. Pretraining Language Models with Human PreferencesarXiv:2302.08582Tomasz Korbak, Kejian Shi, Angelica Chen, et al.2023 · 121 citations
33. ExT5: Towards Extreme Multi-Task Scaling for Transfer LearningarXiv:2111.10952Vamsi Aribandi, Yi Tay, Tal Schuster, et al.2021 · 114 citations
34. Red-Teaming Large Language Models using Chain of Utterances for Safety-AlignmentarXiv:2308.09662Rishabh Bhardwaj, Soujanya Poria2023 · 108 citations
35. Inverse Scaling: When Bigger Isn't BetterarXiv:2306.09479 · Transactions on Machine Learning Research (TMLR), 10/2023, https://openreview.net/forum?id=DwgRm72GQFIan R. McKenzie, Alexander Lyzhov, Michael Pieler, et al.2023 · 105 citations
36. Rephrasing the Web: A Recipe for Compute and Data-Efficient Language ModelingAnnual Meeting of the Association for Computational Linguistics (ACL)Pratyush Maini, Skyler Seto, Richard Bai, et al.2024 · 90 citations
37. Foundational Challenges in Assuring Alignment and Safety of Large Language ModelsarXiv:2404.09932Usman Anwar, Abulhair Saparov, Javier Rando, et al.2024 · 87 citations
38. A Wolf in Sheep's Clothing: Generalized Nested Jailbreak Prompts can Fool Large Language Models EasilyarXiv:2311.08268Peng Ding, Jun Kuang, Dan Ma, et al.2023 · 85 citations
39. Large Language Models in Law: A SurveyarXiv:2312.03718Jinqi Lai, Wensheng Gan, Jiayang Wu, et al.2023 · 80 citations
40. Measuring Political Bias in Large Language Models: What Is Said and How It Is SaidAnnual Meeting of the Association for Computational Linguistics (ACL)Yejin Bang, Delong Chen, Nayeon Lee, et al.2024 · 76 citations
41. The Ghost in the Machine has an American accent: value conflict in GPT-3arXiv:2203.07785Rebecca L Johnson, Giada Pistilli, Natalia Menedez-Gonzalez, et al.2022 · 72 citations
42. Aligning Language Models with Preferences through f-divergence MinimizationarXiv:2302.08215Dongyoung Go, Tomasz Korbak, German Kruszewski, et al.2023 · 39 citations
43. DADA: Dialect Adaptation via Dynamic Aggregation of Linguistic RulesYanchen Liu, William Held, Diyi Yang2023 · 20 citations
44. Identifying and Adapting Transformer-Components Responsible for Gender Bias in an English Language ModelBlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLPAbhijith Chintam, Rahel Beloch, Willem Zuidema, et al.2023 · 19 citations
45. Alignment for Efficient Tool Calling of Large Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Hongshen Xu, Zihan Wang, Zichen Zhu, et al.2025 · 13 citations

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