🔗 Who cites «Reducing Sentiment Bias in Language Models via Counterfactual Evaluation»

82 papers of our library cite it (of the 149,439 whose bibliographies we read) · 23 with a page here, the most cited first · ← back to «Reducing Sentiment Bias in Language Models via Counterfactual Evaluation»

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. Challenges in Detoxifying Language ModelsFindings of the Association for Computational LinguisticsJohannes Welbl, Amelia Glaese, Jonathan Uesato, et al.2021 · 213 citations
14. 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
15. “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
16. 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
17. 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
18. Alignment of Language AgentsarXiv:2103.14659Zachary Kenton, Tom Everitt, Laura Weidinger, et al.2021 · 120 citations
19. 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
20. 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
21. 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
22. 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
23. 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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