---
title: "Who cites Process for Adapting Language — 100 citing papers"
description: "100 papers of the Elamur Research library cite «Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasets»…"
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---
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# 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»](https://md.research.elamur.ai/free/en/papers/solaiman-2021-process-adapting-language-models-society/)

1. [**GPT-4 Technical Report arXiv:2303.08774**OpenAI, Josh Achiam, Steven Adler, et al.*2023 · 13,036 citations*](https://md.research.elamur.ai/free/en/papers/openai-2023-gpt-4-technical-report/)
2. [**Training language models to follow instructions with human feedback arXiv:2203.02155**Long Ouyang, Jeff Wu, Xu Jiang, et al.*2022 · 10,224 citations*](https://md.research.elamur.ai/free/en/papers/ouyang-2022-training-language-models-follow-instructions/)
3. [**On the Opportunities and Risks of Foundation Models arXiv:2108.07258**Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.*2021 · 3,160 citations*](https://md.research.elamur.ai/free/en/papers/bommasani-2021-opportunities-risks-foundation-models/)
4. **[TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://md.research.elamur.ai/free/en/papers/lin-2022-truthfulqa-measuring-how-models-mimic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Stephanie Lin, Jacob Hilton, Owain Evans*2022 · 2,088 citations*
5. [**Finetuned Language Models Are Zero-Shot Learners arXiv:2109.01652**Jason Wei, Maarten Bosma, Vincent Y. Zhao, et al.*2021 · 1,954 citations*](https://md.research.elamur.ai/free/en/papers/wei-2021-finetuned-language-models-zero-shot/)
6. [**LaMDA: Language Models for Dialog Applications arXiv:2201.08239**Romal Thoppilan, Daniel De Freitas, Jamie Hall, et al.*2022 · 1,344 citations*](https://md.research.elamur.ai/free/en/papers/thoppilan-2022-lamda-language-models-dialog-applications/)
7. **[The Rise and Potential of Large Language Model Based Agents: A Survey](https://md.research.elamur.ai/free/en/papers/xi-2023-rise-potential-large-language/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Zhiheng Xi, Wenxiang Chen, Xin Guo, et al.*2023 · 1,272 citations*
8. **[Bias and Fairness in Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/gallegos-2023-bias-fairness-large-language-models/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow, et al.*2023 · 1,082 citations*
9. [**Constitutional AI: Harmlessness from AI Feedback arXiv:2212.08073**Yuntao Bai, Saurav Kadavath, Sandipan Kundu, et al.*2022 · 1,028 citations*](https://md.research.elamur.ai/free/en/papers/bai-2022-constitutional-ai-harmlessness-ai-feedback/)
10. **[Can Large Language Models Transform Computational Social Science?](https://md.research.elamur.ai/free/en/papers/ziems-2023-large-language-models-transform-computational/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Caleb Ziems, William Held, Omar Shaikh, et al.*2023 · 790 citations*
11. **[Red Teaming Language Models with Language Models](https://md.research.elamur.ai/free/en/papers/perez-2022-red-teaming-language-models-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ethan Perez, Saffron Huang, Francis Song, et al.*2022 · 700 citations*
12. **[Jailbroken: How Does LLM Safety Training Fail?](https://md.research.elamur.ai/free/en/papers/wei-2023-jailbroken-llm-safety-training-fail/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Alexander Wei, Nika Haghtalab, Jacob Steinhardt*2023 · 688 citations*
13. [**Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned arXiv:2209.07858**Deep Ganguli, Liane Lovitt, Jackson Kernion, et al.*2022 · 485 citations*](https://md.research.elamur.ai/free/en/papers/ganguli-2022-red-teaming-language-models-reduce/)
14. [**Ethical and social risks of harm from Language Models arXiv:2112.04359**Laura Weidinger, John Mellor, Maribeth Rauh, et al.*2021 · 456 citations*](https://md.research.elamur.ai/free/en/papers/weidinger-2021-ethical-social-risks-harm-language/)
15. [**Text and Code Embeddings by Contrastive Pre-Training arXiv:2201.10005**Arvind Neelakantan, Tao Xu, Raul Puri, et al.*2022 · 323 citations*](https://md.research.elamur.ai/free/en/papers/neelakantan-2022-text-code-embeddings-contrastive-pre/)
16. [**Challenges and Applications of Large Language Models arXiv:2307.10169**Jean Kaddour, Joshua Harris, Maximilian Mozes, et al.*2023 · 301 citations*](https://md.research.elamur.ai/free/en/papers/kaddour-2023-challenges-applications-large-language-models/)
17. **[Challenges and Strategies in Cross-Cultural NLP](https://md.research.elamur.ai/free/en/papers/hershcovich-2022-challenges-strategies-cross-cultural-nlp/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Daniel Hershcovich, Stella Frank, Heather Lent, et al.*2022 · 292 citations*
18. [**Towards Measuring the Representation of Subjective Global Opinions in Language Models arXiv:2306.16388**Esin Durmus, Karina Nguyen, Thomas I. Liao, et al.*2023 · 270 citations*](https://md.research.elamur.ai/free/en/papers/durmus-2023-towards-measuring-representation-subjective-global/)
19. **[Predictability and Surprise in Large Generative Models](https://md.research.elamur.ai/free/en/papers/ganguli-2022-predictability-surprise-large-generative-models/) [ACM Conference on Fairness, Accountability, and Transparency (FAccT)](https://md.research.elamur.ai/free/en/venues/acm-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 Models](https://md.research.elamur.ai/free/en/papers/wang-2023-decodingtrust-comprehensive-assessment-trustworthiness-gpt/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Boxin Wang, Weixin Chen, Hengzhi Pei, et al.*2023 · 259 citations*
21. [**Red teaming ChatGPT via Jailbreaking: Bias, Robustness, Reliability and Toxicity arXiv:2301.12867**Terry Yue Zhuo, Yujin Huang, Chunyang Chen, et al.*2023 · 249 citations*](https://md.research.elamur.ai/free/en/papers/zhuo-2023-red-teaming-chatgpt-jailbreaking-bias/)
22. **[Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision](https://md.research.elamur.ai/free/en/papers/sun-2023-principle-driven-self-alignment-language/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zhiqing Sun, Yikang Shen, Qinhong Zhou, et al.*2023 · 236 citations*
23. [**Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment arXiv:2308.05374**Yang Liu; Yuanshun Yao; Jean-Francois Ton; Xiaoying Zhang; Ruocheng Guo; Hao Cheng; Yegor Klochkov; Muhammad Faaiz Taufiq;, Hang Li*2023 · 223 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-trustworthy-llms-survey-guideline-evaluating/)
24. [**Prompting GPT-3 To Be Reliable arXiv:2210.09150**Chenglei Si, Zhe Gan, Zhengyuan Yang, et al.*2022 · 210 citations*](https://md.research.elamur.ai/free/en/papers/si-2022-prompting-gpt-3-reliable/)
25. **[On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning](https://md.research.elamur.ai/free/en/papers/shaikh-2023-second-thought-let-s-not/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Omar Shaikh, Hongxin Zhang, William Held, et al.*2023 · 192 citations*
26. [**GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts arXiv:2309.10253**Jiahao Yu, Xingwei Lin, Zheng Yu, et al.*2023 · 169 citations*](https://md.research.elamur.ai/free/en/papers/yu-2023-gptfuzzer-red-teaming-large-language/)
27. [**GPT-4 Is Too Smart To Be Safe: Stealthy Chat with LLMs via Cipher arXiv:2308.06463**Youliang Yuan, Wenxiang Jiao, Wenxuan Wang, et al.*2023 · 168 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2023-gpt-4-too-smart-safe/)
28. **[Probing Pre-Trained Language Models for Cross-Cultural Differences in Values](https://md.research.elamur.ai/free/en/papers/arora-2023-probing-pre-trained-language-models/) [Workshop on Cross-Cultural Considerations in NLP](https://md.research.elamur.ai/free/en/venues/c3nlp/)**Arnav Arora, Lucie-aimée Kaffee, Isabelle Augenstein*2023 · 156 citations*
29. [**A Roadmap to Pluralistic Alignment arXiv:2402.05070**Taylor Sorensen, Jared Moore, Jillian Fisher, et al.*2024 · 143 citations*](https://md.research.elamur.ai/free/en/papers/sorensen-2024-roadmap-pluralistic-alignment/)
30. [**Self-Alignment with Instruction Backtranslation arXiv:2308.06259**Xian Li, Ping Yu, Chunting Zhou, et al.*2023 · 139 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-self-alignment-instruction-backtranslation/)
31. **[Evaluating the Moral Beliefs Encoded in LLMs](https://md.research.elamur.ai/free/en/papers/scherrer-2023-evaluating-moral-beliefs-encoded-llms/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Nino Scherrer, Claudia Shi, Amir Feder, et al.*2023 · 129 citations*
32. [**Pretraining Language Models with Human Preferences arXiv:2302.08582**Tomasz Korbak, Kejian Shi, Angelica Chen, et al.*2023 · 121 citations*](https://md.research.elamur.ai/free/en/papers/korbak-2023-pretraining-language-models-human-preferences/)
33. [**ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning arXiv:2111.10952**Vamsi Aribandi, Yi Tay, Tal Schuster, et al.*2021 · 114 citations*](https://md.research.elamur.ai/free/en/papers/aribandi-2021-ext5-towards-extreme-multi-task/)
34. [**Red-Teaming Large Language Models using Chain of Utterances for Safety-Alignment arXiv:2308.09662**Rishabh Bhardwaj, Soujanya Poria*2023 · 108 citations*](https://md.research.elamur.ai/free/en/papers/bhardwaj-2023-red-teaming-large-language-models/)
35. [**Inverse Scaling: When Bigger Isn't Better arXiv:2306.09479 · Transactions on Machine Learning Research (TMLR), 10/2023, https://openreview.net/forum?id=DwgRm72GQF**Ian R. McKenzie, Alexander Lyzhov, Michael Pieler, et al.*2023 · 105 citations*](https://md.research.elamur.ai/free/en/papers/mckenzie-2023-inverse-scaling-bigger-isn-t/)
36. **[Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modeling](https://md.research.elamur.ai/free/en/papers/maini-2024-rephrasing-web-recipe-compute-data/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Pratyush Maini, Skyler Seto, Richard Bai, et al.*2024 · 90 citations*
37. [**Foundational Challenges in Assuring Alignment and Safety of Large Language Models arXiv:2404.09932**Usman Anwar, Abulhair Saparov, Javier Rando, et al.*2024 · 87 citations*](https://md.research.elamur.ai/free/en/papers/anwar-2024-foundational-challenges-assuring-alignment-safety/)
38. [**A Wolf in Sheep's Clothing: Generalized Nested Jailbreak Prompts can Fool Large Language Models Easily arXiv:2311.08268**Peng Ding, Jun Kuang, Dan Ma, et al.*2023 · 85 citations*](https://md.research.elamur.ai/free/en/papers/ding-2023-wolf-sheep-s-clothing-generalized/)
39. [**Large Language Models in Law: A Survey arXiv:2312.03718**Jinqi Lai, Wensheng Gan, Jiayang Wu, et al.*2023 · 80 citations*](https://md.research.elamur.ai/free/en/papers/lai-2023-large-language-models-law-survey/)
40. **[Measuring Political Bias in Large Language Models: What Is Said and How It Is Said](https://md.research.elamur.ai/free/en/papers/bang-2024-measuring-political-bias-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/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-3 arXiv:2203.07785**Rebecca L Johnson, Giada Pistilli, Natalia Menedez-Gonzalez, et al.*2022 · 72 citations*](https://md.research.elamur.ai/free/en/papers/johnson-2022-ghost-machine-has-american-accent/)
42. [**Aligning Language Models with Preferences through f-divergence Minimization arXiv:2302.08215**Dongyoung Go, Tomasz Korbak, German Kruszewski, et al.*2023 · 39 citations*](https://md.research.elamur.ai/free/en/papers/go-2023-aligning-language-models-preferences-through/)
43. [**DADA: Dialect Adaptation via Dynamic Aggregation of Linguistic Rules**Yanchen Liu, William Held, Diyi Yang*2023 · 20 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-dada-dialect-adaptation-dynamic-aggregation/)
44. **[Identifying and Adapting Transformer-Components Responsible for Gender Bias in an English Language Model](https://md.research.elamur.ai/free/en/papers/chintam-2023-identifying-adapting-transformer-components-responsible/) [BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP](https://md.research.elamur.ai/free/en/venues/blackboxnlp/)**Abhijith Chintam, Rahel Beloch, Willem Zuidema, et al.*2023 · 19 citations*
45. **[Alignment for Efficient Tool Calling of Large Language Models](https://md.research.elamur.ai/free/en/papers/xu-2025-alignment-efficient-tool-calling-large/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Hongshen Xu, Zihan Wang, Zichen Zhu, et al.*2025 · 13 citations*

55 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/solaiman-2021-process-adapting-language-models-society/) — Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasets