---
title: "Who cites Seraphina Goldfarb-Tarrant — 168 citing papers"
description: "168 papers of the Elamur Research library cite the works of Seraphina Goldfarb-Tarrant, found in their bibliographies; the most cited first."
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---
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# 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](https://md.research.elamur.ai/free/en/authors/seraphina-goldfarb-tarrant/)

1. [**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/)
2. **[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*
3. [**PaLM 2 Technical Report arXiv:2305.10403**Rohan Anil, Andrew M. Dai, Orhan Firat, et al.*2023 · 1,000 citations*](https://md.research.elamur.ai/free/en/papers/anil-2023-palm-2-technical-report/)
4. **[From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models](https://md.research.elamur.ai/free/en/papers/feng-2023-pretraining-data-language-models-downstream/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shangbin Feng, Chan Young Park, Yuhan Liu, et al.*2023 · 311 citations*
5. **[Gender Bias in Machine Translation](https://md.research.elamur.ai/free/en/papers/savoldi-2021-gender-bias-machine-translation/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/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 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*
7. [**Aya Expanse: Combining Research Breakthroughs for a New Multilingual Frontier arXiv:2412.04261**John Dang, Shivalika Singh, Daniel D'souza, et al.*2024 · 184 citations*](https://md.research.elamur.ai/free/en/papers/dang-2024-aya-expanse-combining-research-breakthroughs/)
8. **[Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation](https://md.research.elamur.ai/free/en/papers/singh-2025-global-mmlu-understanding-addressing-cultural/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/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 Dataset](https://md.research.elamur.ai/free/en/papers/smith-2022-i-m-sorry-hear-that/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Eric Michael Smith, Melissa Hall, Melanie Kambadur, et al.*2022 · 167 citations*
10. **[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*
11. **[Quantifying Social Biases in NLP: A Generalization and Empirical Comparison of Extrinsic Fairness Metrics](https://md.research.elamur.ai/free/en/papers/czarnowska-2021-quantifying-social-biases-nlp-generalization/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Paula Czarnowska, Yogarshi Vyas, Kashif Shah*2021 · 133 citations*
12. **[French CrowS-Pairs: Extending a challenge dataset for measuring social bias in masked language models to a language other than English](https://md.research.elamur.ai/free/en/papers/neveol-2022-french-crows-pairs-extending-challenge/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/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 Art](https://md.research.elamur.ai/free/en/papers/liu-2025-culturally-aware-adapted-nlp-taxonomy/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Chen Cecilia Liu, Iryna Gurevych, Anna Korhonen*2025 · 90 citations*
14. [**INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge arXiv:2411.19799**Angelika Romanou, Negar Foroutan, Anna Sotnikova, et al.*2024 · 74 citations*](https://md.research.elamur.ai/free/en/papers/romanou-2024-include-evaluating-multilingual-language-understanding/)
15. **[Sociodemographic Bias in Language Models: A Survey and Forward Path](https://md.research.elamur.ai/free/en/papers/gupta-2023-sociodemographic-bias-language-models-survey/) [Workshop on Gender Bias in Natural Language Processing](https://md.research.elamur.ai/free/en/venues/gebnlp/)**Vipul 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 Safety arXiv:2404.05399**Paul Rottger, Fabio Pernisi, Bertie Vidgen, et al.*2024 · 33 citations*](https://md.research.elamur.ai/free/en/papers/rottger-2024-safetyprompts-systematic-review-open-datasets/)
17. **[M-RewardBench: Evaluating Reward Models in Multilingual Settings](https://md.research.elamur.ai/free/en/papers/gureja-2025-m-rewardbench-evaluating-reward-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/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 It](https://md.research.elamur.ai/free/en/papers/yong-2025-state-multilingual-llm-safety-research/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/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 Text](https://md.research.elamur.ai/free/en/papers/havens-2022-uncertainty-inclusivity-gender-bias-annotation/) [Workshop on Gender Bias in Natural Language Processing](https://md.research.elamur.ai/free/en/venues/gebnlp/)**Lucy Havens, Melissa Terras, Benjamin Bach, et al.*2022 · 14 citations*
20. **[Safer or Luckier? LLMs as Safety Evaluators Are Not Robust to Artifacts](https://md.research.elamur.ai/free/en/papers/chen-2025-safer-or-luckier-llms-as/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hongyu Chen, Seraphina Goldfarb-Tarrant*2025 · 13 citations*
21. [**Multilingual Arbitrage: Optimizing Data Pools to Accelerate Multilingual Progress arXiv:2408.14960**Ayomide Odumakinde, Daniel D'souza, Pat Verga, et al.*2024 · 12 citations*](https://md.research.elamur.ai/free/en/papers/odumakinde-2024-multilingual-arbitrage-optimizing-data-pools/)
22. **[Speciesist Language and Nonhuman Animal Bias in English Masked Language Models](https://md.research.elamur.ai/free/en/papers/takeshita-2022-speciesist-language-nonhuman-animal-bias/) [Information Processing & Management](https://md.research.elamur.ai/free/en/venues/information-processing-management/)**Masashi Takeshita, Rafal Rzepka, Kenji Araki*2022 · 11 citations*
23. **[MPO: Multilingual Safety Alignment via Reward Gap Optimization](https://md.research.elamur.ai/free/en/papers/zhao-2025-mpo-multilingual-safety-alignment-reward/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/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 LLMs](https://md.research.elamur.ai/free/en/papers/khairi-2025-when-life-gives-you-samples/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ammar Khairi, Daniel D'souza, Ye Shen, et al.*2025 · 6 citations*
25. **[How Long Reasoning Chains Influence LLMs' Judgment of Answer Factuality](https://md.research.elamur.ai/free/en/papers/tu-2026-how-long-reasoning-chains-influence/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Minzhu Tu, Shiyu Ni, Keping Bi*2026 · 3 citations*
26. **[Investigating the Capabilities and Limitations of Machine Learning for Identifying Bias in English Language Data with Information and Heritage Professionals](https://md.research.elamur.ai/free/en/papers/havens-2025-investigating-capabilities-limitations-machine-learning/) [CHI Conference on Human Factors in Computing Systems](https://md.research.elamur.ai/free/en/venues/chi-conference-on-human-factors-in-computing-systems/)**Lucy Havens, Benjamin Bach, Melissa Terras, et al.*2025 · 2 citations*
27. **[Uncertainty Quantification for Evaluating Gender Bias in Machine Translation](https://md.research.elamur.ai/free/en/papers/staliunaite-2026-uncertainty-quantification-evaluating-gender-bias/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ieva Staliunaite, Julius Cheng, Andreas Vlachos*2026 · 1 citation*
28. **[When Models Outthink Their Safety: Unveiling and Mitigating Self-Jailbreak in Large Reasoning Models](https://md.research.elamur.ai/free/en/papers/mao-2026-when-models-outthink-their-safety/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yingzhi Mao, Chunkang Zhang, Junxiang Wang, et al.*2026 · 1 citation*
29. **[Whose Facts Win? LLM Source Preferences under Knowledge Conflicts](https://md.research.elamur.ai/free/en/papers/schuster-2026-whose-facts-win-llm-source/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jakob Schuster, Vagrant Gautam, Katja Markert*2026 · 1 citation*
30. **[AnswerCarefully: Creating a Dataset for LLM Safety in Japanese](https://md.research.elamur.ai/free/en/papers/suzuki-2026-answercarefully-creating-dataset-llm-safety/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Hisami Suzuki, Satoru Katsumata, Takashi Kodama, et al.*2026*
31. **[Bias Dynamics in BabyLMs: Towards a Compute-Efficient Sandbox for Democratising Pre-Training Debiasing](https://md.research.elamur.ai/free/en/papers/trhlik-2026-bias-dynamics-babylms-compute-efficient/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Filip Trhlík, Andrew Caines, Paula Buttery*2026*
32. **[Bias Mitigation in Hiring-Related NLP: Interactions Between Masking, Rewriting, and Adversarial Debiasing](https://md.research.elamur.ai/free/en/papers/puttick-2026-bias-mitigation-hiring-related-nlp/) [International Conference on Natural Language Processing for the Digital Humanities](https://md.research.elamur.ai/free/en/venues/nlp4dh/)**Alexandre Puttick, Rami El-Wazzi*2026*
33. **[How Far Can Bias Go? Tracing Bias from Pre-Training Data to Alignment](https://md.research.elamur.ai/free/en/papers/thaler-2026-how-far-can-bias-go/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Marion Thaler, Abdullatif Köksal, Alina Leidinger, et al.*2026*
34. **[M²PO: Multi-Perspective Multi-Pair Preference Optimization for Machine Translation](https://md.research.elamur.ai/free/en/papers/wang-2026-m-2-po-multi-perspective/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hao Wang, Linlong Xu, Heng Liu, et al.*2026*
35. **[Quantifying Metric and Model Agreement in Bias Evaluation of Large Language Models](https://md.research.elamur.ai/free/en/papers/asgari-2026-quantifying-metric-model-agreement-bias/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Arash Asgari, Huan Wu, Amirreza Naziri, et al.*2026*
36. [**WinoQueer-NL: Assessing Bias in Dutch Language Models toward LGBTQ+ Identities arXiv:2609.02651**Jiska Beuk, Gerasimos Spanakis*2026*](https://md.research.elamur.ai/free/en/papers/beuk-2026-winoqueer-nl-assessing-bias-dutch/)

132 more citing papers of our library have no page here yet.

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