🧑‍🔬 Who cites Vishal Maini

191 citations of their works from 188 papers of our library (of the 149,439 whose bibliographies we read) · 69 with a page here, the most cited first · ← Vishal Maini

1. Language Models are Few-Shot LearnersarXiv:2005.14165Tom B. Brown, Benjamin Mann, Nick Ryder, et al.2020 · 14,178 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. Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelsarXiv:2206.04615 · Transactions on Machine Learning Research, May/2022, https://openreview.net/forum?id=uyTL5BvosjAarohi Srivastava, Abhinav Rastogi, Abhishek Rao, et al.2022 · 1,756 citations
5. Language (Technology) is Power: A Critical Survey of “Bias” in NLPAnnual Meeting of the Association for Computational Linguistics (ACL)Su Lin Blodgett, Solon Barocas, Hal Daumé III, et al.2020 · 1,716 citations
6. LaMDA: Language Models for Dialog ApplicationsarXiv:2201.08239Romal Thoppilan, Daniel De Freitas, Jamie Hall, et al.2022 · 1,344 citations
7. Bias and Fairness in Large Language Models: A SurveyComputational LinguisticsIsabel O. Gallegos, Ryan A. Rossi, Joe Barrow, et al.2023 · 1,082 citations
8. GPQA: A Graduate-Level Google-Proof Q&A BenchmarkarXiv:2311.12022David Rein, Betty Li Hou, Asa Cooper Stickland, et al.2023 · 1,018 citations
9. Universal and Transferable Adversarial Attacks on Aligned Language ModelsarXiv:2307.15043Andy Zou, Zifan Wang, Nicholas Carlini, et al.2023 · 1,005 citations
10. CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Nikita Nangia, Clara Vania, Rasika Bhalerao, et al.2020 · 897 citations
11. WebGPT: Browser-assisted question-answering with human feedbackarXiv:2112.09332Reiichiro Nakano, Jacob Hilton, Suchir Balaji, et al.2021 · 721 citations
12. 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
13. Release Strategies and the Social Impacts of Language ModelsarXiv:1908.09203Irene Solaiman, Miles Brundage, Jack Clark, et al.2019 · 554 citations
14. Ethical and social risks of harm from Language ModelsarXiv:2112.04359Laura Weidinger, John Mellor, Maribeth Rauh, et al.2021 · 456 citations
15. Tulu 3: Pushing Frontiers in Open Language Model Post-TrainingarXiv:2411.15124Nathan Lambert, Jacob Morrison, Valentina Pyatkin, et al.2024 · 423 citations
16. Towards Understanding Sycophancy in Language ModelsarXiv:2310.13548Mrinank Sharma, Meg Tong, Tomasz Korbak, et al.2023 · 384 citations
17. BeaverTails: Towards Improved Safety Alignment of LLM via a Human-Preference DatasetConference on Neural Information Processing Systems (NeurIPS)Jiaming Ji, Mickel Liu, Juntao Dai, et al.2023 · 362 citations
18. Improving alignment of dialogue agents via targeted human judgementsarXiv:2209.14375Amelia Glaese, Nat McAleese, Maja Trębacz, et al.2022 · 362 citations
19. Scaling Laws for Reward Model OveroptimizationarXiv:2210.10760Leo Gao, John Schulman, Jacob Hilton2022 · 323 citations
20. Towards Understanding and Mitigating Social Biases in Language ModelsarXiv:2106.13219Paul Pu Liang, Chiyu Wu, Louis-Philippe Morency, et al.2021 · 271 citations
21. Societal Biases in Language Generation: Progress and ChallengesAnnual Meeting of the Association for Computational Linguistics (ACL)Emily Sheng, Kai-Wei Chang, Prem Natarajan, et al.2021 · 257 citations
22. RAFT: Reward rAnked FineTuning for Generative Foundation Model AlignmentarXiv:2304.06767Hanze Dong, Wei Xiong, Deepanshu Goyal, et al.2023 · 252 citations
23. Teaching language models to support answers with verified quotesarXiv:2203.11147Jacob Menick, Maja Trebacz, Vladimir Mikulik, et al.2022 · 218 citations
24. RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human FeedbackIEEE Computer Society Conference on Computer Vision and Pattern RecognitionTianyu Yu, Yuan Yao, Haoye Zhang, et al.2023 · 217 citations
25. Challenges in Detoxifying Language ModelsFindings of the Association for Computational LinguisticsJohannes Welbl, Amelia Glaese, Jonathan Uesato, et al.2021 · 213 citations
26. Recursively Summarizing Books with Human FeedbackarXiv:2109.10862Jeff Wu, Long Ouyang, Daniel M. Ziegler, et al.2021 · 208 citations
27. AI Alignment: A Comprehensive SurveyarXiv:2310.19852Jiaming Ji, Tianyi Qiu, Boyuan Chen, et al.2023 · 173 citations
28. Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak SupervisionarXiv:2312.09390Collin Burns, Pavel Izmailov, Jan Hendrik Kirchner, et al.2023 · 172 citations
29. Paragraph-level Rationale Extraction through Regularization: A case study on European Court of Human Rights CasesConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Ilias Chalkidis, Manos Fergadiotis, Dimitrios Tsarapatsanis, et al.2021 · 168 citations
30. “I'm sorry to hear that”: Finding New Biases in Language Models with a Holistic Descriptor DatasetConference on Empirical Methods in Natural Language Processing (EMNLP)Eric Michael Smith, Melissa Hall, Melanie Kambadur, et al.2022 · 167 citations
31. Process Reinforcement through Implicit RewardsarXiv:2502.01456Ganqu Cui, Lifan Yuan, Zefan Wang, et al.2025 · 151 citations
32. Unsolved Problems in ML SafetyarXiv:2109.13916Dan Hendrycks, Nicholas Carlini, John Schulman, et al.2021 · 151 citations
33. A Roadmap to Pluralistic AlignmentarXiv:2402.05070Taylor Sorensen, Jared Moore, Jillian Fisher, et al.2024 · 143 citations
34. Humans or LLMs as the Judge? A Study on Judgement BiasConference on Empirical Methods in Natural Language Processing (EMNLP)Guiming Hardy Chen, Shunian Chen, Ziche Liu, et al.2024 · 133 citations
35. Quantifying Social Biases in NLP: A Generalization and Empirical Comparison of Extrinsic Fairness MetricsTransactions of the Association for Computational Linguistics (TACL)Paula Czarnowska, Yogarshi Vyas, Kashif Shah2021 · 133 citations
36. You reap what you sow: On the Challenges of Bias Evaluation Under Multilingual SettingsBigScience Episode – Workshop on Challenges & Perspectives in Creating Large Language ModelsZeerak Talat, Aurélie Névéol, Stella Biderman, et al.2022 · 130 citations
37. Large Language Model Alignment: A SurveyarXiv:2309.15025Tianhao Shen, Renren Jin, Yufei Huang, et al.2023 · 124 citations
38. Alignment of Language AgentsarXiv:2103.14659Zachary Kenton, Tom Everitt, Laura Weidinger, et al.2021 · 120 citations
39. Best Practices and Lessons Learned on Synthetic DataarXiv:2404.07503Ruibo Liu, Jerry Wei, Fangyu Liu, et al.2024 · 119 citations
40. Measuring Progress on Scalable Oversight for Large Language ModelsarXiv:2211.03540Samuel R. Bowman, Jeeyoon Hyun, Ethan Perez, et al.2022 · 117 citations
41. On the Intrinsic and Extrinsic Fairness Evaluation Metrics for Contextualized Language RepresentationsAnnual Meeting of the Association for Computational Linguistics (ACL)Yang Trista Cao, Yada Pruksachatkun, Kai-Wei Chang, et al.2022 · 100 citations
42. Safety Alignment Should Be Made More Than Just a Few Tokens DeeparXiv:2406.05946Xiangyu Qi, Ashwinee Panda, Kaifeng Lyu, et al.2024 · 97 citations
43. Training Socially Aligned Language Models on Simulated Social InteractionsarXiv:2305.16960Ruibo Liu, Ruixin Yang, Chenyan Jia, et al.2023 · 94 citations
44. A Survey of Reinforcement Learning from Human FeedbackTransactions on Machine Learning ResearchTimo Kaufmann, Paul Weng, Viktor Bengs, et al.2023 · 92 citations
45. Foundational Challenges in Assuring Alignment and Safety of Large Language ModelsarXiv:2404.09932Usman Anwar, Abulhair Saparov, Javier Rando, et al.2024 · 87 citations
46. Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language GenerationTransactions of the Association for Computational Linguistics (TACL)Patrick Fernandes, Aman Madaan, Emmy Liu, et al.2023 · 84 citations
47. Humans or LLMs as the Judge? A Study on Judgement BiasesarXiv:2402.10669Guiming Hardy Chen, Shunian Chen, Ziche Liu, et al.2024 · 78 citations
48. Secrets of RLHF in Large Language Models Part II: Reward ModelingarXiv:2401.06080Binghai Wang, Rui Zheng, Lu Chen, et al.2024 · 72 citations
49. Modular Pluralism: Pluralistic Alignment via Multi-LLM CollaborationConference on Empirical Methods in Natural Language Processing (EMNLP)Shangbin Feng, Taylor Sorensen, Yuhan Liu, et al.2024 · 68 citations
50. MLLM-Protector: Ensuring MLLM's Safety without Hurting PerformanceConference on Empirical Methods in Natural Language Processing (EMNLP)Renjie Pi, Tianyang Han, Jianshu Zhang, et al.2024 · 58 citations
51. Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedbackarXiv:2303.05453Hannah Rose Kirk, Bertie Vidgen, Paul Rottger, et al.2023 · 57 citations
52. The Multilingual Alignment Prism: Aligning Global and Local Preferences to Reduce HarmConference on Empirical Methods in Natural Language Processing (EMNLP)Aakanksha, Arash Ahmadian, Beyza Ermis, et al.2024 · 49 citations
53. HyperCLOVA X Technical ReportarXiv:2404.01954Kang Min Yoo, Jaegeun Han, Sookyo In, et al.2024 · 44 citations
54. When does In-context Learning Fall Short and Why? A Study on Specification-Heavy TasksarXiv:2311.08993Hao Peng, Xiaozhi Wang, Jianhui Chen, et al.2023 · 42 citations
55. SALMON: Self-Alignment with Instructable Reward ModelsarXiv:2310.05910Zhiqing Sun, Yikang Shen, Hongxin Zhang, et al.2023 · 41 citations
56. Sociodemographic Bias in Language Models: A Survey and Forward PathWorkshop on Gender Bias in Natural Language ProcessingVipul Gupta, Pranav Narayanan Venkit, Shomir Wilson, et al.2023 · 40 citations
57. Aligning LLMs with Individual Preferences via InteractionInternational Conference on Computational Linguistics (COLING)Shujin Wu, Yi R. Fung, Cheng Qian, et al.2025 · 24 citations
58. Rewarding Chatbots for Real-World Engagement with Millions of UsersarXiv:2303.06135Robert Irvine, Douglas Boubert, Vyas Raina, et al.2023 · 22 citations
59. CRAFT Your Dataset: Task-Specific Synthetic Dataset Generation Through Corpus Retrieval and AugmentationTransactions of the Association for Computational Linguistics (TACL)Ingo Ziegler, Abdullatif Köksal, Desmond Elliott, et al.2025 · 20 citations
60. The Dark Side of Function Calling: Pathways to Jailbreaking Large Language ModelsInternational Conference on Computational Linguistics (COLING)Zihui Wu, Haichang Gao, Jianping He, et al.2025 · 8 citations
61. CoAct: Co-Active LLM Preference Learning with Human-AI SynergyAnnual Meeting of the Association for Computational Linguistics (ACL)Ruiyao Xu, Mihir Parmar, Tiankai Yang, et al.2026 · 1 citation
62. Incentivizing Strong Reasoning from Weak SupervisionConference of the European Chapter of the Association for Computational Linguistics (EACL)Yige Yuan, Teng Xiao, Shuchang Tao, et al.2026 · 1 citation
63. When Do Large Language Models Exhibit Unsolicited Deception?Transactions of the Association for Computational Linguistics (TACL)Samuel M. Taylor, Benjamin K. Bergen2026 · 1 citation
64. Entity-Level Sentiment Analysis with Sentence Relevance DetectionLanguage Resources and Evaluation Conference (LREC)Egil Rønningstad, Roman Klinger, Lilja Øvrelid, et al.2026
65. Generalizing Trust: Weak-to-Strong Trustworthiness in Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Lillian Sun, Martin Pawelczyk, Zhenting Qi, et al.2026
66. How Far Can Bias Go? Tracing Bias from Pre-Training Data to AlignmentLanguage Resources and Evaluation Conference (LREC)Marion Thaler, Abdullatif Köksal, Alina Leidinger, et al.2026
67. PubMed Reasoner: Dynamic Reasoning-based Retrieval for Evidence-Grounded Biomedical Question AnsweringAnnual Meeting of the Association for Computational Linguistics (ACL)Yiqing Zhang, Xiaozhong Liu, Fabricio Murai2026
68. QueerGen: How LLMs Reflect Societal Norms on Gender and Sexuality in Sentence Completion TaskFindings of the Association for Computational LinguisticsMae Sosto, Delfina S. Martinez Pandiani, Laura Hollink2026
69. Too Open for Opinion? Embracing Open-Endedness in Large Language Models for Social SimulationConference of the European Chapter of the Association for Computational Linguistics (EACL)Bolei Ma, Yong Cao, Indira Sen, et al.2026

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