Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection
Open accessAbstract
Bertie Vidgen, Tristan Thrush, Zeerak Waseem, Douwe Kiela. 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). 2021.
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
LLMs can be prompted to generate harmful or toxic content across adversarial contexts.
“LLMs can be prompted to generate harmful or toxic content across adversarial contexts.”
From Verifying Intent and Harm: A Unified Defense Against LLM-Generated Threats (2026) · cites this paper as [24]
Cites
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv · doi:10.48550/arxiv.1907.11692 · Cited by 16,515 · see citing works
Beyond accuracy: behavioral testing of NLP models with CheckList
Ribeiro, M. T., Wu, T., Guestrin, C., & Singh, S. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.442 · Cited by 890 · see citing works
Bender, E. M., & Friedman, B. (2018) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00041 · Cited by 854 · cited 1× · see citing works
“Following Bender and Friedman (2018) we provide a data statement, which documents the process and provenance of the final dataset.”
MethodThe Risk of Racial Bias in Hate Speech Detection
Sap, M., Card, D., Gabriel, S., Choi, Y., & Smith, N. A. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1163 · Cited by 817 · see citing works
Measuring and Mitigating Unintended Bias in Text Classification
Dixon, L., Li, J., Sorensen, J., Thain, N., & Vasserman, L. (2018) · Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3278721.3278729 · Cited by 674 · see citing works
Geva, M., Goldberg, Y., & Berant, J. (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/d19-1107 · Cited by 284 · see citing works
Build it Break it Fix it for Dialogue Safety: Robustness from Adversarial Human Attack
Dinan, E., Humeau, S., Chintagunta, B., & Weston, J. (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/d19-1461 · Cited by 161 · see citing works
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ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection
Hartvigsen, T., Gabriel, S., Palangi, H., Sap, M., Ray, D., & Kamar, E. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.234 · Cited by 279 · 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
Self-citationBBQ: 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
StarCoder 2 and The Stack v2: The Next Generation
Lozhkov, A., Li, R., Allal, L. B., Cassano, F., Lamy-Poirier, J., Tazi, N., Tang, A., Pykhtar, D., Liu, J., Wei, Y., et al. (2024) · arXiv · doi:10.48550/arxiv.2402.19173 · Cited by 74 · see citing works
Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora
Warstadt, A., Mueller, A., Choshen, L., Wilcox, E., Zhuang, C., Ciro, J., Mosquera, R., Paranjabe, B., Williams, A., Linzen, T., et al. (2023) · Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning · doi:10.18653/v1/2023.conll-babylm.1 · Cited by 74 · see citing works
Quantifying and alleviating political bias in language models
Liu, R., Jia, C., Wei, J., Xu, G., & Vosoughi, S. (2022) · Artificial Intelligence · doi:10.1016/j.artint.2021.103654 · Cited by 68 · see citing works
Hate speech detection: A comprehensive review of recent works
Gandhi, A., Ahir, P., Adhvaryu, K., Shah, P., Lohiya, R., Cambria, E., Poria, S., & Hussain, A. (2024) · Expert Systems · doi:10.1111/exsy.13562 · Cited by 66 · see citing works
Data-Efficient Strategies for Expanding Hate Speech Detection into Under-Resourced Languages
Röttger, P., Nozza, D., Bianchi, F., & Hovy, D. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.383 · Cited by 18 · see citing works
NLPositionality: Characterizing Design Biases of Datasets and Models
Santy, S., Liang, J. T., Bras, R. L., Reinecke, K., & Sap, M. (2023) · arXiv · doi:10.48550/arxiv.2306.01943 · Cited by 31 · see citing works
“Similarly, Dynahate exhibits high correlation with their annotator populations, which are mostly women, White, 18-29 years old, native English speakers, and British (Vidgen et al., 2021).”
BackgroundMethodInfluentialVerifying Intent and Harm: A Unified Defense Against LLM-Generated Threats
Thota, P., Lei, Y., Thangaraj, S., Jonnalagadda, S. R., & Nilizadeh, S. (2026) · arXiv · doi:10.48550/arxiv.2606.26377
“Content moderation has evolved from rule-based filters and BERT-based toxicity classifiers [23], [24] to LLM-based guardrail models.”
Analyzing Toxicity in Deep Conversations: A Reddit Case Study
Shankaran, V., & Sharma, R. (2024) · arXiv · doi:10.48550/arxiv.2404.07879
- Authors
- Vidgen, B., Thrush, T., Waseem, Z., & Kiela, D.
- Affiliations
- Turing Institute (GB)
- The Alan Turing Institute (GB)
- Meta (Israel) (IL)
- ORCID
- Vidgen, Bertie
- Venue
- 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)
- Published
- 2021
- Pages
- 1667-1682
- OpenAlex
- W3128611684
- Cited by
- 174
- References
- 7
- Citations by year
- 2026: 21 · 2025: 40 · 2024: 25 · 2023: 41 · 2022: 33 · 2021: 13 · 2020: 1
- FWCI
- 12.94
- Type
- conference paper
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- adversarial examples, training data quality, training data bias
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