---
title: "Who cites Tony Sun — 267 papers of our library"
description: "267 papers of the Elamur Research library cite the works of Tony Sun, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/tony-sun/cited-by/"
---
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# Who cites Tony Sun

285 citations of their works from 267 papers of our library (of the 141,840 whose bibliographies we read) · 37 with a page here, the most cited first · [← Tony Sun](https://md.research.elamur.ai/free/en/authors/tony-sun/)

1. **[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 · 1,703 citations*
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 · 1,074 citations*
3. **[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*
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 · 306 citations*
5. **[Challenges and Strategies in Cross-Cultural NLP](https://md.research.elamur.ai/free/en/papers/hershcovich-2022-challenges-strategies-cross-cultural-nlp/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Daniel Hershcovich, Stella Frank, Heather Lent, et al.*2022 · 290 citations*
6. **[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 · 266 citations*
7. [**Towards Understanding and Mitigating Social Biases in Language Models arXiv:2106.13219**Paul Pu Liang, Chiyu Wu, Louis-Philippe Morency, et al.*2021 · 265 citations*](https://md.research.elamur.ai/free/en/papers/liang-2021-towards-understanding-mitigating-social-biases/)
8. **[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 · 259 citations*
9. **[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 · 253 citations*
10. **[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 · 248 citations*
11. **[Automatic Detection of Machine Generated Text: A Critical Survey](https://md.research.elamur.ai/free/en/papers/jawahar-2020-automatic-detection-machine-generated-text/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Ganesh Jawahar, Muhammad Abdul-Mageed, Laks Lakshmanan V.S.*2020 · 245 citations*
12. **[Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies](https://md.research.elamur.ai/free/en/papers/dev-2021-harms-gender-exclusivity-challenges-non/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Sunipa Dev, Masoud Monajatipoor, Anaelia Ovalle, et al.*2021 · 228 citations*
13. [**Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment arXiv:2308.05374**Yang Liu; Yuanshun Yao; Jean-Francois Ton; Xiaoying Zhang; Ruocheng Guo; Hao Cheng; Yegor Klochkov; Muhammad Faaiz Taufiq;, Hang Li*2023 · 220 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-trustworthy-llms-survey-guideline-evaluating/)
14. [**HopeEDI: A Multilingual Hope Speech Detection Dataset for Equality, Diversity, and Inclusion Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Media**Bharathi Raja Chakravarthi*2020 · 211 citations*](https://md.research.elamur.ai/free/en/papers/chakravarthi-2020-hopeedi-multilingual-hope-speech-detection/)
15. **[Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview](https://md.research.elamur.ai/free/en/papers/shah-2019-predictive-biases-natural-language-processing/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Deven Shah, H. Andrew Schwartz, Dirk Hovy*2019 · 202 citations*
16. **[Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation Problem](https://md.research.elamur.ai/free/en/papers/saunders-2020-reducing-gender-bias-neural-machine/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Danielle Saunders, Bill Byrne*2020 · 196 citations*
17. **[Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation](https://md.research.elamur.ai/free/en/papers/vanmassenhove-2021-machine-translationese-effects-algorithmic-bias/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Eva Vanmassenhove, Dimitar Shterionov, Matthew Gwilliam*2021 · 161 citations*
18. **[Measure and Improve Robustness in NLP Models: A Survey](https://md.research.elamur.ai/free/en/papers/wang-2022-measure-improve-robustness-nlp-models/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Xuezhi Wang, Haohan Wang, Diyi Yang*2022 · 148 citations*
19. **[UNQOVERing Stereotyping Biases via Underspecified Questions](https://md.research.elamur.ai/free/en/papers/li-2020-unqovering-stereotyping-biases-underspecified-questions/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Tao Li, Daniel Khashabi, Tushar Khot, et al.*2020 · 145 citations*
20. **[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 · 131 citations*
21. **[You reap what you sow: On the Challenges of Bias Evaluation Under Multilingual Settings](https://md.research.elamur.ai/free/en/papers/talat-2022-you-reap-what-you-sow/) [BigScience Episode – Workshop on Challenges & Perspectives in Creating Large Language Models](https://md.research.elamur.ai/free/en/venues/bigscience/)**Zeerak Talat, Aurélie Névéol, Stella Biderman, et al.*2022 · 130 citations*
22. **[Unmasking Contextual Stereotypes: Measuring and Mitigating BERT's Gender Bias](https://md.research.elamur.ai/free/en/papers/bartl-2020-unmasking-contextual-stereotypes-measuring-mitigating/) [Workshop on Gender Bias in Natural Language Processing](https://md.research.elamur.ai/free/en/venues/gebnlp/)**Marion Bartl, Malvina Nissim, Albert Gatt*2020 · 115 citations*
23. [**From Persona to Personalization: A Survey on Role-Playing Language Agents arXiv:2404.18231**Jiangjie Chen, Xintao Wang, Rui Xu, et al.*2024 · 114 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-persona-personalization-survey-role-playing/)
24. **[Gender in Danger? Evaluating Speech Translation Technology on the MuST-SHE Corpus](https://md.research.elamur.ai/free/en/papers/bentivogli-2020-gender-danger-evaluating-speech-translation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Luisa Bentivogli, Beatrice Savoldi, Matteo Negri, et al.*2020 · 100 citations*
25. **[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 · 89 citations*
26. **[Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey](https://md.research.elamur.ai/free/en/papers/kumar-2023-language-generation-models-can-cause/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Sachin Kumar, Vidhisha Balachandran, Lucille Njoo, et al.*2023 · 87 citations*
27. **[Neural Machine Translation Doesn't Translate Gender Coreference Right Unless You Make It](https://md.research.elamur.ai/free/en/papers/saunders-2020-neural-machine-translation-doesn-t/) [Workshop on Gender Bias in Natural Language Processing](https://md.research.elamur.ai/free/en/venues/gebnlp/)**Danielle Saunders, Rosie Sallis, Bill Byrne*2020 · 86 citations*
28. **[NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation](https://md.research.elamur.ai/free/en/papers/dhole-2023-nl-augmenter-framework-task-sensitive/) [Northern European Journal of Language Technology](https://md.research.elamur.ai/free/en/venues/northern-european-journal-of-language-technology/)**Kaustubh Dhole, Varun Gangal, Sebastian Gehrmann, et al.*2023 · 85 citations*
29. [**They, Them, Theirs: Rewriting with Gender-Neutral English arXiv:2102.06788**Tony Sun, Kellie Webster, Apu Shah, et al.*2021 · 71 citations*](https://md.research.elamur.ai/free/en/papers/sun-2021-they-them-theirs-rewriting-gender/)
30. **[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*
31. **[Improving Arabic Diacritization by Learning to Diacritize and Translate](https://md.research.elamur.ai/free/en/papers/thompson-2022-improving-arabic-diacritization-learning-diacritize/) [International Conference on Spoken Language Translation (IWSLT)](https://md.research.elamur.ai/free/en/venues/iwslt/)**Brian Thompson, Ali Alshehri*2022 · 12 citations*
32. **[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*
33. **[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*
34. **[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*
35. **[Gender Disambiguation in Machine Translation: Diagnostic Evaluation in Decoder-Only Architectures](https://md.research.elamur.ai/free/en/papers/manna-2026-gender-disambiguation-machine-translation-diagnostic/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Chiara Manna, Hosein Mohebbi, Afra Alishahi, et al.*2026 · 3 citations*
36. **[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*
37. **[Rethinking Gender Annotation for Bias Evaluation in Machine Translation: Can LLMs Improve Reliability?](https://md.research.elamur.ai/free/en/papers/manna-2026-rethinking-gender-annotation-bias-evaluation/) [Workshop on Gender-Inclusive Translation Technologies](https://md.research.elamur.ai/free/en/venues/gitt/)**Chiara Manna, Argentina Anna Rescigno, Eva Vanmassenhove*2026*

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

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