🧑‍🔬 Who cites Seraphina Goldfarb-Tarrant

169 citations of their works from 168 papers of our library (of the 149,439 whose bibliographies we read) · 36 with a page here, the most cited first · ← Seraphina Goldfarb-Tarrant

1. On the Opportunities and Risks of Foundation ModelsarXiv:2108.07258Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.2021 · 3,160 citations
2. Bias and Fairness in Large Language Models: A SurveyComputational LinguisticsIsabel O. Gallegos, Ryan A. Rossi, Joe Barrow, et al.2023 · 1,082 citations
3. PaLM 2 Technical ReportarXiv:2305.10403Rohan Anil, Andrew M. Dai, Orhan Firat, et al.2023 · 1,000 citations
4. From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Shangbin Feng, Chan Young Park, Yuhan Liu, et al.2023 · 311 citations
5. Gender Bias in Machine TranslationTransactions of the Association for Computational Linguistics (TACL)Beatrice Savoldi, Marco Gaido, Luisa Bentivogli, et al.2021 · 310 citations
6. 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
7. Aya Expanse: Combining Research Breakthroughs for a New Multilingual FrontierarXiv:2412.04261John Dang, Shivalika Singh, Daniel D'souza, et al.2024 · 184 citations
8. Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual EvaluationAnnual Meeting of the Association for Computational Linguistics (ACL)Shivalika Singh, Angelika Romanou, Clémentine Fourrier, et al.2025 · 182 citations
9. “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
10. 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
11. 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
12. French CrowS-Pairs: Extending a challenge dataset for measuring social bias in masked language models to a language other than EnglishAnnual Meeting of the Association for Computational Linguistics (ACL)Aurélie Névéol, Yoann Dupont, Julien Bezançon, et al.2022 · 107 citations
13. Culturally Aware and Adapted NLP: A Taxonomy and a Survey of the State of the ArtTransactions of the Association for Computational Linguistics (TACL)Chen Cecilia Liu, Iryna Gurevych, Anna Korhonen2025 · 90 citations
14. INCLUDE: Evaluating Multilingual Language Understanding with Regional KnowledgearXiv:2411.19799Angelika Romanou, Negar Foroutan, Anna Sotnikova, et al.2024 · 74 citations
15. 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
16. SafetyPrompts: a Systematic Review of Open Datasets for Evaluating and Improving Large Language Model SafetyarXiv:2404.05399Paul Rottger, Fabio Pernisi, Bertie Vidgen, et al.2024 · 33 citations
17. M-RewardBench: Evaluating Reward Models in Multilingual SettingsAnnual Meeting of the Association for Computational Linguistics (ACL)Srishti Gureja, Lester James V. Miranda, Shayekh Bin Islam, et al.2025 · 28 citations
18. The State of Multilingual LLM Safety Research: From Measuring The Language Gap To Mitigating ItConference on Empirical Methods in Natural Language Processing (EMNLP)Zheng Xin Yong, Beyza Ermis, Marzieh Fadaee, et al.2025 · 25 citations
19. Uncertainty and Inclusivity in Gender Bias Annotation: An Annotation Taxonomy and Annotated Datasets of British English TextWorkshop on Gender Bias in Natural Language ProcessingLucy Havens, Melissa Terras, Benjamin Bach, et al.2022 · 14 citations
20. Safer or Luckier? LLMs as Safety Evaluators Are Not Robust to ArtifactsAnnual Meeting of the Association for Computational Linguistics (ACL)Hongyu Chen, Seraphina Goldfarb-Tarrant2025 · 13 citations
21. Multilingual Arbitrage: Optimizing Data Pools to Accelerate Multilingual ProgressarXiv:2408.14960Ayomide Odumakinde, Daniel D'souza, Pat Verga, et al.2024 · 12 citations
22. Speciesist Language and Nonhuman Animal Bias in English Masked Language ModelsInformation Processing & ManagementMasashi Takeshita, Rafal Rzepka, Kenji Araki2022 · 11 citations
23. MPO: Multilingual Safety Alignment via Reward Gap OptimizationAnnual Meeting of the Association for Computational Linguistics (ACL)Weixiang Zhao, Yulin Hu, Yang Deng, et al.2025 · 7 citations
24. When Life Gives You Samples: The Benefits of Scaling up Inference Compute for Multilingual LLMsConference on Empirical Methods in Natural Language Processing (EMNLP)Ammar Khairi, Daniel D'souza, Ye Shen, et al.2025 · 6 citations
25. How Long Reasoning Chains Influence LLMs' Judgment of Answer FactualityAnnual Meeting of the Association for Computational Linguistics (ACL)Minzhu Tu, Shiyu Ni, Keping Bi2026 · 3 citations
26. Investigating the Capabilities and Limitations of Machine Learning for Identifying Bias in English Language Data with Information and Heritage ProfessionalsCHI Conference on Human Factors in Computing SystemsLucy Havens, Benjamin Bach, Melissa Terras, et al.2025 · 2 citations
27. Uncertainty Quantification for Evaluating Gender Bias in Machine TranslationFindings of the Association for Computational LinguisticsIeva Staliunaite, Julius Cheng, Andreas Vlachos2026 · 1 citation
28. When Models Outthink Their Safety: Unveiling and Mitigating Self-Jailbreak in Large Reasoning ModelsFindings of the Association for Computational LinguisticsYingzhi Mao, Chunkang Zhang, Junxiang Wang, et al.2026 · 1 citation
29. Whose Facts Win? LLM Source Preferences under Knowledge ConflictsAnnual Meeting of the Association for Computational Linguistics (ACL)Jakob Schuster, Vagrant Gautam, Katja Markert2026 · 1 citation
30. AnswerCarefully: Creating a Dataset for LLM Safety in JapaneseLanguage Resources and Evaluation Conference (LREC)Hisami Suzuki, Satoru Katsumata, Takashi Kodama, et al.2026
31. Bias Dynamics in BabyLMs: Towards a Compute-Efficient Sandbox for Democratising Pre-Training DebiasingFindings of the Association for Computational LinguisticsFilip Trhlík, Andrew Caines, Paula Buttery2026
32. Bias Mitigation in Hiring-Related NLP: Interactions Between Masking, Rewriting, and Adversarial DebiasingInternational Conference on Natural Language Processing for the Digital HumanitiesAlexandre Puttick, Rami El-Wazzi2026
33. 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
34. M²PO: Multi-Perspective Multi-Pair Preference Optimization for Machine TranslationAnnual Meeting of the Association for Computational Linguistics (ACL)Hao Wang, Linlong Xu, Heng Liu, et al.2026
35. Quantifying Metric and Model Agreement in Bias Evaluation of Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Arash Asgari, Huan Wu, Amirreza Naziri, et al.2026
36. WinoQueer-NL: Assessing Bias in Dutch Language Models toward LGBTQ+ IdentitiesarXiv:2609.02651Jiska Beuk, Gerasimos Spanakis2026

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