---
title: "Who cites Zeyu Li — 185 papers of our library — Elamur Research"
description: "185 papers of the Elamur Research library cite the works of Zeyu Li, found in their bibliographies; the most cited first."
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---
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# Who cites Zeyu Li

185 citations of their works from 185 papers of our library (of the 149,439 whose bibliographies we read) · 41 with a page here, the most cited first · [← Zeyu Li](https://md.research.elamur.ai/free/en/authors/zeyu-li/)

1. **[A Survey on Bias and Fairness in Machine Learning](https://md.research.elamur.ai/free/en/papers/mehrabi-2019-survey-bias-fairness-machine-learning/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, et al.*2019 · 5,638 citations*
2. **[Language (Technology) is Power: A Critical Survey of “Bias” in NLP](https://md.research.elamur.ai/free/en/papers/blodgett-2020-language-technology-is-power-critical/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Su Lin Blodgett, Solon Barocas, Hal Daumé III, et al.*2020 · 1,716 citations*
3. **[Mitigating Gender Bias in Natural Language Processing: Literature Review](https://md.research.elamur.ai/free/en/papers/sun-2019-mitigating-gender-bias-natural-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tony Sun, Andrew Gaut, Shirlyn Tang, et al.*2019 · 724 citations*
4. **[Gender Bias in Contextualized Word Embeddings](https://md.research.elamur.ai/free/en/papers/zhao-2019-gender-bias-contextualized-word-embeddings/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Jieyu Zhao, Tianlu Wang, Mark Yatskar, et al.*2019 · 508 citations*
5. **[Null It Out: Guarding Protected Attributes by Iterative Nullspace Projection](https://md.research.elamur.ai/free/en/papers/ravfogel-2020-null-it-out-guarding-protected/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shauli Ravfogel, Yanai Elazar, Hila Gonen, et al.*2020 · 488 citations*
6. [**Identifying and Reducing Gender Bias in Word-Level Language Models arXiv:1904.03035**Shikha Bordia, Samuel R. Bowman*2019 · 367 citations*](https://md.research.elamur.ai/free/en/papers/bordia-2019-identifying-reducing-gender-bias-word/)
7. **[Toxicity in chatgpt: Analyzing persona-assigned language models](https://md.research.elamur.ai/free/en/papers/deshpande-2023-toxicity-chatgpt-analyzing-persona-assigned/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ameet Deshpande, Vishvak Murahari, Tanmay Rajpurohit, et al.*2023 · 353 citations*
8. **[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*
9. [**Towards Understanding and Mitigating Social Biases in Language Models arXiv:2106.13219**Paul Pu Liang, Chiyu Wu, Louis-Philippe Morency, et al.*2021 · 271 citations*](https://md.research.elamur.ai/free/en/papers/liang-2021-towards-understanding-mitigating-social-biases/)
10. **[Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation](https://md.research.elamur.ai/free/en/papers/dinan-2020-queens-are-powerful-too-mitigating/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Emily Dinan, Angela Fan, Adina Williams, et al.*2020 · 270 citations*
11. **[Towards Debiasing Sentence Representations](https://md.research.elamur.ai/free/en/papers/liang-2020-debiasing-sentence-representations/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Paul Pu Liang, Irene Mengze Li, Emily Zheng, et al.*2020 · 262 citations*
12. **[Unlearn Dataset Bias in Natural Language Inference by Fitting the Residual](https://md.research.elamur.ai/free/en/papers/he-2019-unlearn-dataset-bias-natural-language/) [Workshop on Deep Learning Approaches for Low-Resource NLP](https://md.research.elamur.ai/free/en/venues/workshop-on-deep-learning-approaches-for-low-resource-nlp/)**He He, Sheng Zha, Haohan Wang*2019 · 260 citations*
13. **[Societal Biases in Language Generation: Progress and Challenges](https://md.research.elamur.ai/free/en/papers/sheng-2021-societal-biases-language-generation-progress/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Emily Sheng, Kai-Wei Chang, Prem Natarajan, et al.*2021 · 257 citations*
14. [**Measuring and Reducing Gendered Correlations in Pre-trained Models arXiv:2010.06032**Kellie Webster, Xuezhi Wang, Ian Tenney, et al.*2020 · 254 citations*](https://md.research.elamur.ai/free/en/papers/webster-2020-measuring-reducing-gendered-correlations-pre/)
15. **[Interpreting Pretrained Contextualized Representations via Reductions to Static Embeddings](https://md.research.elamur.ai/free/en/papers/bommasani-2020-interpreting-pretrained-contextualized-representations-reductions/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Rishi Bommasani, Kelly Davis, Claire Cardie*2020 · 250 citations*
16. [**BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage arXiv:2208.03188**Kurt Shuster, Jing Xu, Mojtaba Komeili, et al.*2022 · 241 citations*](https://md.research.elamur.ai/free/en/papers/shuster-2022-blenderbot-3-deployed-conversational-agent/)
17. [**Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP arXiv:2103.00453**Timo Schick, Sahana Udupa, Hinrich Schutze*2021 · 213 citations*](https://md.research.elamur.ai/free/en/papers/schick-2021-self-diagnosis-self-debiasing-proposal/)
18. **[Auto-Debias: Debiasing Masked Language Models with Automated Biased Prompts](https://md.research.elamur.ai/free/en/papers/guo-2022-auto-debias-debiasing-masked-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yue Guo, Yi Yang, Ahmed Abbasi*2022 · 191 citations*
19. **[Debiasing Pre-trained Contextualised Embeddings](https://md.research.elamur.ai/free/en/papers/kaneko-2021-debiasing-pre-trained-contextualised-embeddings/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Masahiro Kaneko, Danushka Bollegala*2021 · 175 citations*
20. **[Understanding Undesirable Word Embedding Associations](https://md.research.elamur.ai/free/en/papers/ethayarajh-2019-understanding-undesirable-word-embedding-associations/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kawin Ethayarajh, David Duvenaud, Graeme Hirst*2019 · 172 citations*
21. **[Towards Controllable Biases in Language Generation](https://md.research.elamur.ai/free/en/papers/sheng-2020-controllable-biases-language-generation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Emily Sheng, Kai-Wei Chang, Prem Natarajan, et al.*2020 · 171 citations*
22. **[Does Gender Matter? Towards Fairness in Dialogue Systems](https://md.research.elamur.ai/free/en/papers/liu-2020-does-gender-matter-fairness-dialogue/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Haochen Liu, Jamell Dacon, Wenqi Fan, et al.*2020 · 133 citations*
23. **[Toward Gender-Inclusive Coreference Resolution](https://md.research.elamur.ai/free/en/papers/cao-2020-gender-inclusive-coreference-resolution/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yang Trista Cao, Hal Daumé III*2020 · 127 citations*
24. **[Intrinsic Bias Metrics Do Not Correlate with Application Bias](https://md.research.elamur.ai/free/en/papers/tarrant-2021-intrinsic-bias-metrics-do-not/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Seraphina Goldfarb-Tarrant, Rebecca Marchant, Ricardo Muñoz Sánchez, et al.*2021 · 117 citations*
25. **[On the Safety of Conversational Models: Taxonomy, Dataset, and Benchmark](https://md.research.elamur.ai/free/en/papers/sun-2022-safety-conversational-models-taxonomy-dataset/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hao Sun, Guangxuan Xu, Jiawen Deng, et al.*2022 · 102 citations*
26. **[Automatic Gender Identification and Reinflection in Arabic](https://md.research.elamur.ai/free/en/papers/habash-2019-automatic-gender-identification-reinflection-arabic/) [Workshop on Gender Bias in Natural Language Processing](https://md.research.elamur.ai/free/en/venues/gebnlp/)**Nizar Habash, Houda Bouamor, Christine Chung*2019 · 75 citations*
27. **[Toward Gender-Inclusive Coreference Resolution: An Analysis of Gender and Bias Throughout the Machine Learning Lifecycle*](https://md.research.elamur.ai/free/en/papers/cao-2021-gender-inclusive-coreference-resolution-analysis/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Yang Trista Cao, Hal Daumé III*2021 · 72 citations*
28. **[Towards Robustifying NLI Models Against Lexical Dataset Biases](https://md.research.elamur.ai/free/en/papers/zhou-2020-robustifying-nli-models-against-lexical/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiang Zhou, Mohit Bansal*2020 · 70 citations*
29. **[Bias in Large Language Models: Origin, Evaluation, and Mitigation](https://md.research.elamur.ai/free/en/papers/guo-2024-bias-large-language-models-origin/) [Electronics](https://md.research.elamur.ai/free/en/venues/electronics/)**Yufei Guo, Muzhe Guo, Juntao Su, et al.*2024 · 58 citations*
30. **[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*
31. **[BERT Knows Punta Cana is not just beautiful, it's gorgeous: Ranking Scalar Adjectives with Contextualised Representations](https://md.research.elamur.ai/free/en/papers/soler-2020-bert-knows-punta-cana-is/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Aina Garí Soler, Marianna Apidianaki*2020 · 30 citations*
32. **[User-Centric Gender Rewriting](https://md.research.elamur.ai/free/en/papers/alhafni-2022-user-centric-gender-rewriting/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Bashar Alhafni, Nizar Habash, Houda Bouamor*2022 · 29 citations*
33. **[The Arabic Parallel Gender Corpus 2.0: Extensions and Analyses](https://md.research.elamur.ai/free/en/papers/alhafni-2022-arabic-parallel-gender-corpus-2/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Bashar Alhafni, Nizar Habash, Houda Bouamor*2022 · 21 citations*
34. **[Marked Attribute Bias in Natural Language Inference](https://md.research.elamur.ai/free/en/papers/dawkins-2021-marked-attribute-bias-natural-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hillary Dawkins*2021 · 10 citations*
35. **[Evaluating and Mitigating Inherent Linguistic Bias of African American English through Inference](https://md.research.elamur.ai/free/en/papers/dacon-2022-evaluating-mitigating-inherent-linguistic-bias/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Jamell Dacon, Haochen Liu, Jiliang Tang*2022 · 6 citations*
36. **[The User-Aware Arabic Gender Rewriter](https://md.research.elamur.ai/free/en/papers/alhafni-2023-user-aware-arabic-gender-rewriter/) [Workshop on Gender-Inclusive Translation Technologies](https://md.research.elamur.ai/free/en/venues/gitt/)**Bashar Alhafni, Ossama Obeid, Nizar Habash*2023 · 5 citations*
37. **[The Shared Task on Gender Rewriting](https://md.research.elamur.ai/free/en/papers/alhafni-2022-shared-task-gender-rewriting/) [Arabic Natural Language Processing Workshop (WANLP)](https://md.research.elamur.ai/free/en/venues/wanlp/)**Bashar Alhafni, Nizar Habash, Houda Bouamor, et al.*2022 · 3 citations*
38. **[ColorBrowserAgent: Complex Long-Horizon Browser Agent with Adaptive Knowledge Evolution](https://md.research.elamur.ai/free/en/papers/wang-2026-colorbrowseragent-complex-long-horizon-browser/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jihong Wang, Jiamu Zhou, Weiming Zhang, et al.*2026 · 1 citation*
39. **[Beyond Reasoning Gains: Mitigating General-Capability Forgetting in Large Reasoning Models](https://md.research.elamur.ai/free/en/papers/phan-2026-beyond-reasoning-gains-mitigating-general/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hoang Phan, Xianjun Yang, Yuanshun Yao, et al.*2026*
40. **[Caged Birds and Cute Bookworms: Feminine Tropes and Implicit Gender Bias in Large Language Models](https://md.research.elamur.ai/free/en/papers/nishal-2026-caged-birds-cute-bookworms-feminine/) [Workshop on Evaluating Evaluations (EvalEval)](https://md.research.elamur.ai/free/en/venues/evaleval/)**Sachita Nishal, Jack Bandy*2026*
41. **[GKnow: Measuring the Entanglement of Gender Bias and Factual Gender](https://md.research.elamur.ai/free/en/papers/veloso-2026-gknow-measuring-entanglement-gender-bias/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Leonor Veloso, Hinrich Schuetze*2026*

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

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