🧑‍🔬 Who cites Robert Stanforth

142 citations of their works from 138 papers of our library (of the 149,439 whose bibliographies we read) · 28 with a page here, the most cited first · ← Robert Stanforth

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. 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
4. LaMDA: Language Models for Dialog ApplicationsarXiv:2201.08239Romal Thoppilan, Daniel De Freitas, Jamie Hall, et al.2022 · 1,344 citations
5. Bias and Fairness in Large Language Models: A SurveyComputational LinguisticsIsabel O. Gallegos, Ryan A. Rossi, Joe Barrow, et al.2023 · 1,082 citations
6. 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
7. 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
8. Release Strategies and the Social Impacts of Language ModelsarXiv:1908.09203Irene Solaiman, Miles Brundage, Jack Clark, et al.2019 · 554 citations
9. Ethical and social risks of harm from Language ModelsarXiv:2112.04359Laura Weidinger, John Mellor, Maribeth Rauh, et al.2021 · 456 citations
10. 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
11. Towards Understanding and Mitigating Social Biases in Language ModelsarXiv:2106.13219Paul Pu Liang, Chiyu Wu, Louis-Philippe Morency, et al.2021 · 271 citations
12. 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
13. TrustLLM: Trustworthiness in Large Language ModelsarXiv:2401.05561Yue Huang, Lichao Sun, Haoran Wang, et al.2024 · 237 citations
14. Challenges in Detoxifying Language ModelsFindings of the Association for Computational LinguisticsJohannes Welbl, Amelia Glaese, Jonathan Uesato, et al.2021 · 213 citations
15. A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and ValidationArtificial Intelligence ReviewXiaowei Huang, Wenjie Ruan, Wei Huang, et al.2023 · 199 citations
16. 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
17. “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
18. Reevaluating Adversarial Examples in Natural LanguageFindings of the Association for Computational LinguisticsJohn Morris, Eli Lifland, Jack Lanchantin, et al.2020 · 151 citations
19. 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
20. 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
21. Alignment of Language AgentsarXiv:2103.14659Zachary Kenton, Tom Everitt, Laura Weidinger, et al.2021 · 120 citations
22. Certifying LLM Safety against Adversarial PromptingarXiv:2309.02705Aounon Kumar, Chirag Agarwal, Suraj Srinivas, et al.2023 · 113 citations
23. 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
24. 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
25. The Trickle-down Impact of Reward (In-)consistency on RLHFarXiv:2309.16155Lingfeng Shen, Sihao Chen, Linfeng Song, et al.2023 · 11 citations
26. 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
27. 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
28. 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

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