Gender Bias in Coreference Resolution
Open accessAbstract
Rachel Rudinger, Jason Naradowsky, Brian Leonard, Benjamin Van Durme. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers). 2018.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
InstructGPT does not significantly improve over GPT-3 on the Winogender and CrowS-Pairs datasets.
“InstructGPT does not significantly improve over GPT-3 on the Winogender (Rudinger et al., 2018) and CrowSPairs (Nangia et al., 2020) datasets.”
From Training language models to follow instructions with human feedback (2022) · cites this paper as Rudinger et al., 2018
InstructGPT does not significantly improve over GPT-3 on the Winogender and CrowSPairs datasets.
“InstructGPT does not significantly improve over GPT-3 on the Winogender (Rudinger et al., 2018) and CrowSPairs (Nangia et al., 2020) datasets.”
From Training language models to follow instructions with human feedback (2022) · cites this paper as Rudinger et al., 2018
Winogender differs from WinoBias by only mentioning one occupation, which interacts with a participant rather than another occupation.
“Winogender also differs from WinoBias by only mentioning one occupation, which instead interacts with a participant, rather than another occupation.”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Rudinger et al. 2018
Winograd schemas have been adapted for bias evaluation to measure words' associations with social groups, most prominently with Winogender and WinoBias.
“Winograd schemas have since been adapted for bias evaluation to measure words' associations with social groups, most prominently with Winogender (Rudinger et al. 2018) and WinoBias (Zhao et al. 2018)”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Rudinger et al. 2018
Winogender presents a schema for gender and occupation stereotypes with 720 sentences over 60 occupations.
“Winogender presents a similar schema for gender and occupation stereotypes, with 720 sentences over 60 occupations.”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Rudinger et al. 2018
Winogender includes a neutral pronoun option, whereas WinoBias only provides masculine and feminine pronoun genders.
“While WinoBias only provides masculine and feminine pronoun genders, Winogender also includes a neutral option.”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Rudinger et al. 2018
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Barocas, S., & Selbst, A. D. (2014) · doi:10.2139/ssrn.2477899 · Cited by 3,048 · see citing works
Word embeddings quantify 100 years of gender and ethnic stereotypes
Garg, N., Schiebinger, L., Jurafsky, D., & Zou, J. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1720347115 · Cited by 1,065 · see citing works
Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints
Zhao, J., Wang, T., Yatskar, M., Ordonez, V., & Chang, K.-W. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1323 · Cited by 829 · cited 2× · see citing works
“Finally, we note independent but closely related work by Zhao et al. (2018), published concurrently with this paper.”
MethodResultGender and Dialect Bias in YouTube’s Automatic Captions
Tatman, R. (2017) · Proceedings of the First ACL Workshop on Ethics in Natural Language Processing · doi:10.18653/v1/w17-1606 · Cited by 338 · see citing works
The social impact of natural language processing
Hovy, D., & Spruit, S. L. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/p16-2096 · Cited by 329 · see citing works
Flagged first, then by citations
Research works printed but not linked: 19
- Shane Bergsma and Dekang Lin. 2006. Bootstrapping path-based pronoun resolution. In Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Associ- ation for Computational Linguistics, pages 33-40, Sydney, Australia. Association for Computational Linguistics.
- Alex Beutel, Jilin Chen, Zhe Zhao, and Ed H. Chi. 2017. Data decisions and theoretical implica- tions when adversarially learning fair representa- tions. CoRR, abs/1707.00075. · arXiv
- Anders Björkelund and Jonas Kuhn. 2014. Learn- ing structured perceptrons for coreference resolution with latent antecedents and non-local features. In Proceedings of the 52nd Annual Meeting of the As- sociation for Computational Linguistics (Volume 1: Long Papers), pages 47-57, Baltimore, Maryland. Association for Computational Linguistics.
- Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, and Adam T Kalai. 2016. Man is to computer programmer as woman is to homemaker? debiasing word embeddings. In D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett, editors, Advances in Neural Information Processing Systems 29, pages 4349-4357.
- Aylin Caliskan, Joanna J. Bryson, and Arvind Narayanan. 2017. Semantics derived automatically from language corpora contain human-like biases. Science, 356(6334):183-186.
- Kevin Clark and Christopher D. Manning. 2016a. Deep reinforcement learning for mention-ranking coreference models. In Empirical Methods on Nat- ural Language Processing (EMNLP).
- Kevin Clark and Christopher D. Manning. 2016b. Im- proving coreference resolution by learning entity- level distributed representations. In Association for Computational Linguistics (ACL).
- Greg Durrett and Dan Klein. 2013. Easy victories and uphill battles in coreference resolution. In Proceed- ings of the Conference on Empirical Methods in Nat- ural Language Processing, Seattle, Washington. As- sociation for Computational Linguistics.
- Corina Koolen and Andreas van Cranenburgh. 2017. These are not the stereotypes you are looking for: Bias and fairness in authorial gender attribution. In Proceedings of the First ACL Workshop on Ethics in Natural Language Processing, pages 12-22, Valen- cia, Spain. Association for Computational Linguis- tics.
- Brian Larson. 2017. Gender as a variable in natural- language processing: Ethical considerations. In Proceedings of the First ACL Workshop on Ethics in Natural Language Processing, pages 1-11, Valen- cia, Spain. Association for Computational Linguis- tics.
- Heeyoung Lee, Yves Peirsman, Angel Chang, Nathanael Chambers, Mihai Surdeanu, and Dan Ju- rafsky. 2011. Stanford's multi-pass sieve coref- erence resolution system at the conll-2011 shared task. In Conference on Natural Language Learning (CoNLL) Shared Task.
- Sameer Pradhan, Alessandro Moschitti, Nianwen Xue, Olga Uryupina, and Yuchen Zhang. 2012. CoNLL- 2012 shared task: Modeling multilingual unre- stricted coreference in OntoNotes. In Proceedings of the Sixteenth Conference on Computational Natu- ral Language Learning (CoNLL 2012), Jeju, Korea.
- Sameer Pradhan, Lance Ramshaw, Mitchell Marcus, Martha Palmer, Ralph Weischedel, and Nianwen Xue. 2011. Conll-2011 shared task: Modeling un- restricted coreference in ontonotes. In Proceedings of the Fifteenth Conference on Computational Nat- ural Language Learning (CoNLL 2011), Portland, Oregon.
- Rachel Rudinger, Chandler May, and Benjamin Van Durme. 2017. Social bias in elicited natural lan- guage inferences. In Proceedings of the First ACL Workshop on Ethics in Natural Language Process- ing, pages 74-79, Valencia, Spain. Association for Computational Linguistics.
- Mikaela Wapman and Deborah Belle. 2014. Under- graduate thesis. https://mikaelawapman. com/category/gender-schemas/.
- Sam Wiseman, Alexander M. Rush, Stuart Shieber, and Jason Weston. 2015. Learning anaphoricity and an- tecedent ranking features for coreference resolution. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Lan- guage Processing (Volume 1: Long Papers), pages 1416-1426. Association for Computational Linguis- tics.
- Sam Wiseman, Alexander M. Rush, and Stuart M. Shieber. 2016. Learning global features for coref- erence resolution. In Proceedings of the 2016 Con- ference of the North American Chapter of the Asso- ciation for Computational Linguistics: Human Lan- guage Technologies, pages 994-1004, San Diego, California. Association for Computational Linguis- tics.
- Brian Hu Zhang, Blake Lemoine, and Margaret Mitchell. 2018. Mitigating unwanted biases with adversarial learning. In Proceedings of the First AAAI/ACM Conference on AI, Ethics, and Society.
- Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Or- donez, and Kai-Wei Chang. 2018. Gender bias in coreference resolution: Evaluation and debiasing methods. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Tech- nologies, New Orleans, Louisiana. Association for Computational Linguistics.
Cited by
Sorted by citations
A Survey on Bias and Fairness in Machine Learning
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2022) · ACM Computing Surveys · doi:10.1145/3457607 · Cited by 5,812 · see citing works
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.02155 · Cited by 4,208 · see citing works
PaLM: Scaling Language Modeling with Pathways
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2204.02311 · Cited by 2,590 · see citing works
“…from BIG-bench (BIG-bench collaboration, 2021), reasoning tasks (Section 6.3), code completion tasks (Section 6.4), multilingual generation and question answering tasks (Section 6.6), translation tasks (Section 6.5), and bias and toxicity benchmarks (Rudinger et al., 2018; Gehman et al., 2020).”
BackgroundMethodResultInfluentialScaling Instruction-Finetuned Language Models
Chung, H. W., Hou, L., Longpre, S., Zoph, B., Tay, Y., Fedus, W., Li, Y., Wang, X., Dehghani, M., Brahma, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2210.11416 · Cited by 1,441 · see citing works
A Comprehensive Overview of Large Language Models
Naveed, H., Khan, A. U., Qiu, S., Saqib, M., Anwar, S., Usman, M., Akhtar, N., Barnes, N., & Mian, A. (2025) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3744746 · Cited by 1,257 · see citing works
Language (Technology) is Power: A Critical Survey of “Bias” in NLP
Blodgett, S. L., Barocas, S., Daumé III, H., & Wallach, H. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.485 · Cited by 1,050 · see citing works
“…work on “bias” in systems developed for a breadth of tasks including language modeling (Lu et al., 2018; Bordia and Bowman, 2019), coreference resolution (Rudinger et al., 2018; Zhao et al., 2018a), machine translation (Vanmassenhove et al., 2018; Stanovsky et al., 2019), sentiment analysis…”
BackgroundInfluentialGemini: A Family of Highly Capable Multimodal Models
Gemini Team, Anil, R., Borgeaud, S., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., Millican, K., et al. (2023) · arXiv · doi:10.48550/arxiv.2312.11805 · Cited by 990 · see citing works
Flamingo: a visual language model for few-shot learning
Alayrac, J.-B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., et al. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-1723 · Cited by 966 · see citing works
“7) in terms of gender bias on the Winogender dataset [93] which demonstrate that even though this model is less biased towards gender than previous models [86], gender biases are still present.”
BackgroundBias and Fairness in Large Language Models: A Survey
Gallegos, I. O., Rossi, R. A., Barrow, J., Tanjim, M. M., Kim, S., Dernoncourt, F., Yu, T., Zhang, R., & Ahmed, N. K. (2024) · Computational Linguistics · doi:10.1162/coli_a_00524 · Cited by 665 · see citing works
StereoSet: Measuring stereotypical bias in pretrained language models
Nadeem, M., Bethke, A., & Reddy, S. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.416 · Cited by 643 · see citing works
A Review on Fairness in Machine Learning
Pessach, D., & Shmueli, E. (2023) · ACM Computing Surveys · doi:10.1145/3494672 · Cited by 598 · see citing works
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., Abid, A., Fisch, A., Brown, A. R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2206.04615 · Cited by 569 · see citing works
Mitigating Gender Bias in Natural Language Processing: Literature Review
Sun, T., Gaut, A., Tang, S., Huang, Y., ElSherief, M., Zhao, J., Mirza, D., Belding, E., Chang, K.-W., & Wang, W. Y. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1159 · Cited by 492 · see citing works
CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models
Nangia, N., Vania, C., Bhalerao, R., & Bowman, S. R. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.154 · Cited by 446 · see citing works
The Woman Worked as a Babysitter: On Biases in Language Generation
Sheng, E., Chang, K.-W., Natarajan, P., & Peng, N. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1339 · Cited by 431 · see citing works
“We analyze these two bias contexts because the occupation context has been well-studied in other tasks (Bolukbasi et al., 2016; Rudinger et al., 2018; Zhao et al., 2018; Zhou et al., 2019), and the more descriptive language in respect contexts are a good contrast for the more subtle occupation…”
MethodGender bias and stereotypes in Large Language Models
Kotek, H., Dockum, R., & Sun, D. (2023) · Proceedings of The ACM Collective Intelligence Conference · doi:10.1145/3582269.3615599 · Cited by 381 · see citing works
Measuring Bias in Contextualized Word Representations
Kurita, K., Vyas, N., Pareek, A., Black, A. W., & Tsvetkov, Y. (2019) · Proceedings of the First Workshop on Gender Bias in Natural Language Processing · doi:10.18653/v1/w19-3823 · Cited by 348 · see citing works
Targeted Syntactic Evaluation of Language Models
Marvin, R., & Linzen, T. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1151 · Cited by 334 · see citing works
Gender and Representation Bias in GPT-3 Generated Stories
Lucy, L., & Bamman, D. (2021) · Proceedings of the Third Workshop on Narrative Understanding · doi:10.18653/v1/2021.nuse-1.5 · Cited by 287 · see citing works
Dynabench: Rethinking Benchmarking in NLP
Kiela, D., Bartolo, M., Nie, Y., Kaushik, D., Geiger, A., Wu, Z., Vidgen, B., Prasad, G., Singh, A., Ringshia, P., et al. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.324 · Cited by 254 · see citing works
BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation
Dhamala, J., Sun, T., Kumar, V., Krishna, S., Pruksachatkun, Y., Chang, K.-W., & Gupta, R. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445924 · Cited by 244 · see citing works
Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP
Schick, T., Udupa, S., & Schütze, H. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00434 · Cited by 227 · see citing works
Generative AI in Higher Education: The ChatGPT Effect
Chan, C. K. Y., & Colloton, T. (2024) · Routledge · doi:10.4324/9781003459026 · Cited by 203 · see citing works
Training Compute-Optimal Large Language Models
Hoffmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., Cai, T., Rutherford, E., Casas, D. D. L., Hendricks, L. A., Welbl, J., Clark, A., et al. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-2176 · Cited by 932 · see citing works
“Here, we test if potential gender and occupation biases manifest in unfair outcomes on coreference resolutions, using the Winogender dataset (Rudinger et al., 2018) in a zero-shot setting.”
BBQ: A hand-built bias benchmark for question answering
Parrish, A., Chen, A., Nangia, N., Padmakumar, V., Phang, J., Thompson, J., Htut, P. M., & Bowman, S. (2022) · Findings of the Association for Computational Linguistics: ACL 2022 · doi:10.18653/v1/2022.findings-acl.165 · Cited by 186 · see citing works
Stereotyping Norwegian Salmon: An Inventory of Pitfalls in Fairness Benchmark Datasets
Blodgett, S. L., Lopez, G., Olteanu, A., Sim, R., & Wallach, H. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.81 · Cited by 147 · see citing works
“These datasets consist of tests that can take a variety of formats, including sentence templates where terms pertaining to groups or their attributes are perturbed (Rudinger et al., 2018; Zhao et al., 2018; Kurita et al., 2019), prompts designed to elicit problematic responses (Gehman et al.”
BackgroundInfluentialTowards trustworthy LLMs: a review on debiasing and dehallucinating in large language models
Lin, Z., Guan, S., Zhang, W., Zhang, H., Li, Y., & Zhang, H. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10896-y · Cited by 141 · see citing works
Utility is in the Eye of the User: A Critique of NLP Leaderboards
Ethayarajh, K., & Jurafsky, D. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.393 · Cited by 147 · see citing works
Tenney, I., Wexler, J., Bastings, J., Bolukbasi, T., Coenen, A., Gehrmann, S., Jiang, E., Pushkarna, M., Radebaugh, C., Reif, E., et al. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · doi:10.18653/v1/2020.emnlp-demos.15 · Cited by 138 · see citing works
“Each Winogender example has a pronoun and two candidate referents, one a occupation term like (“technician”) and one an “other participant” (like “customer”).”
MethodReducing Sentiment Bias in Language Models via Counterfactual Evaluation
Huang, P.-S., Zhang, H., Jiang, R., Stanforth, R., Welbl, J., Rae, J., Maini, V., Yogatama, D., & Kohli, P. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.7 · Cited by 124 · see citing works
Garrido-Muñoz, I., Montejo-Ráez, A., Martínez-Santiago, F., & Ureña-López, L. A. (2021) · Applied Sciences · doi:10.3390/app11073184 · Cited by 123 · see citing works
HONEST: Measuring Hurtful Sentence Completion in Language Models
Nozza, D., Bianchi, F., & Hovy, D. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.191 · Cited by 95 · see citing works
Measure and Improve Robustness in NLP Models: A Survey
Wang, X., Wang, H., & Yang, D. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2022.naacl-main.339 · Cited by 90 · see citing works
Impact of Pretraining Term Frequencies on Few-Shot Numerical Reasoning
Razeghi, Y., Logan IV, R. L., Gardner, M., & Singh, S. (2022) · Findings of the Association for Computational Linguistics: EMNLP 2022 · doi:10.18653/v1/2022.findings-emnlp.59 · Cited by 89 · see citing works
On Second Thought, Let’s Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning
Shaikh, O., Zhang, H., Held, W., Bernstein, M., & Yang, D. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.244 · Cited by 76 · see citing works
On Second Thought, Let’s Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning
Shaikh, O., Zhang, H., Held, W., Bernstein, M., & Yang, D. (2022) · arXiv · doi:10.48550/arxiv.2212.08061 · Cited by 76 · see citing works
Czarnowska, P., Vyas, Y., & Shah, K. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00425 · Cited by 72 · see citing works
DynaSent: A Dynamic Benchmark for Sentiment Analysis
Potts, C., Wu, Z., Geiger, A., & Kiela, D. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.186 · Cited by 56 · see citing works
Discovering Language Model Behaviors with Model-Written Evaluations
Perez, E., Ringer, S., Lukošiūtė, K., Nguyen, K., Chen, E., Heiner, S., Pettit, C., Olsson, C., Kundu, S., Kadavath, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2212.09251 · Cited by 53 · see citing works
“12Winogender sentences come in pairs, but we focus on the sentences where the pronoun is coreferent with the person referred to by the occupation.”
MethodInfluentialLyu, L., He, X., & Li, Y. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.213 · Cited by 51 · see citing works
Mitigating Gender Bias in Captioning Systems
Tang, R., Du, M., Li, Y., Liu, Z., Zou, N., & Hu, X. (2021) · Proceedings of the Web Conference 2021 · doi:10.1145/3442381.3449950 · Cited by 50 · see citing works
Cao, Y. T., & Daumé, H. (2021) · Computational Linguistics · doi:10.1162/coli_a_00413 · Cited by 47 · see citing works
A Review of Current Trends, Techniques, and Challenges in Large Language Models (LLMs)
Patil, R., & Gudivada, A. V. (2024) · doi:10.20944/preprints202402.0357.v1 · Cited by 296 · see citing works
Digital forgetting in large language models: a survey of unlearning methods
Blanco-Justicia, A., Jebreel, N., Manzanares-Salor, B., Sánchez, D., Domingo-Ferrer, J., Collell, G., & Eeik Tan, K. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-024-11078-6 · Cited by 31 · see citing works
Gendered Mental Health Stigma in Masked Language Models
Lin, I., Njoo, L., Field, A., Sharma, A., Reinecke, K., Althoff, T., & Tsvetkov, Y. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.139 · Cited by 33 · see citing works
Examining risks of racial biases in NLP tools for child protective services
Field, A., Coston, A., Gandhi, N., Chouldechova, A., Putnam-Hornstein, E., Steier, D., & Tsvetkov, Y. (2023) · 2023 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3593013.3594094 · Cited by 18 · see citing works
KoSBI: A Dataset for Mitigating Social Bias Risks Towards Safer Large Language Model Applications
Lee, H., Hong, S., Park, J., Kim, T., Kim, G., & Ha, J.-W. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track) · doi:10.18653/v1/2023.acl-industry.21 · Cited by 15 · see citing works
Towards Implicit Bias Detection and Mitigation in Multi-Agent LLM Interactions
Borah, A., & Mihalcea, R. (2024) · Findings of the Association for Computational Linguistics: EMNLP 2024 · doi:10.18653/v1/2024.findings-emnlp.545 · Cited by 15 · see citing works
CoSafe: Evaluating Large Language Model Safety in Multi-Turn Dialogue Coreference
Yu, E., Li, J., Liao, M., Wang, S., Gao, Z., Mi, F., & Hong, L. (2024) · arXiv · doi:10.48550/arxiv.2406.17626 · Cited by 7 · see citing works
“Type Purpose WinoGender (Rudinger et al., 2018a) 720 Prompt Evaluate gender bias in coreference resolution.”
BackgroundMethodSmaller Large Language Models Can Do Moral Self-Correction
Liu, G., Xue, Z., Zhang, X., Wang, R., & Johnson, K. (2025) · Proceedings of the 5th Workshop on Trustworthy NLP (TrustNLP 2025) · doi:10.18653/v1/2025.trustnlp-main.5 · Cited by 1
- Authors
- Rudinger, R., Naradowsky, J., Leonard, B., & Van Durme, B.
- Affiliations
- Johns Hopkins University (US)
- ORCID
- Rudinger, Rachel
- Venue
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
- Published
- 2018
- Pages
- 8-14
- OpenAlex
- W2798280648
- Cited by
- 443
- References
- 5
- Citations by year
- 2026: 17 · 2025: 34 · 2024: 41 · 2023: 75 · 2022: 70 · 2021: 85 · 2020: 74 · 2019: 37 · 2018: 10
- FWCI
- 21.02
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1232825)
- Defense Advanced Research Projects Agency
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Social Sciences › Gender Studies › Gender Studies in Language
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- coreference resolution, gender bias, gender-neutral pronouns, gendered language, natural language processing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
