The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
The Other Side of Compression: Measuring Bias in Pruned Transformers
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“Proskurina, Metzler, and Velcin (2023) provide further evidence that aggressive pruning can have adverse effects: for hate speech classification, models with pruning of 30% or more of the original parameters demonstrate increased levels of gender, race, and religious bias.”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Proskurina, Metzler, and Velcin (2023)
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Cites
Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter
Waseem, Z., & Hovy, D. (2016) · Proceedings of the NAACL Student Research Workshop · doi:10.18653/v1/n16-2013 · Cited by 1,655
(2019) · doi:10.18653/v1/p19-1356 · Cited by 1,291
A Survey on Automatic Detection of Hate Speech in Text
Fortuna, P., & Nunes, S. (2019) · ACM Computing Surveys · doi:10.1145/3232676 · Cited by 1,241
HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection
Mathew, B., Saha, P., Yimam, S. M., Biemann, C., Goyal, P., & Mukherjee, A. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i17.17745 · Cited by 513
Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification
Borkan, D., Dixon, L., Sorensen, J., Thain, N., & Vasserman, L. (2019) · Companion Proceedings of The 2019 World Wide Web Conference · doi:10.1145/3308560.3317593 · Cited by 309
Hate speech detection and racial bias mitigation in social media based on BERT model
Mozafari, M., Farahbakhsh, R., & Crespi, N. (2020) · PLOS ONE · doi:10.1371/journal.pone.0237861 · Cited by 260
Steiger, M., Bharucha, T. J., Venkatagiri, S., Riedl, M. J., & Lease, M. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445092 · Cited by 222
What Happens To BERT Embeddings During Fine-tuning?
Merchant, A., Rahimtoroghi, E., Pavlick, E., & Tenney, I. F. (2020) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2020.blackboxnlp-1.4 · Cited by 209
Towards generalisable hate speech detection: a review on obstacles and solutions
Yin, W., & Zubiaga, A. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.598 · Cited by 207
HateCheck: Functional Tests for Hate Speech Detection Models
Röttger, P., Vidgen, B., Nguyen, D., Waseem, Z., Margetts, H., & Pierrehumbert, J. (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.4 · Cited by 187
Research works printed but not linked: 8
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- Liu, Y., et al.: Roberta: a robustly optimized BERT pretraining approach. CoRR abs/1907.11692 (2019). http://arxiv.org/abs/1907.11692 · arXiv
- Neill, J.O.: An overview of neural network compression. arXiv preprint arXiv:2006.03669 (2020) · arXiv
- Niu, J., Lu, W., Penn, G.: Does BERT rediscover a classical NLP pipeline? In: Proceedings of the 29th International Conference on Computational Linguistics, pp. 3143–3153 (2022)
- Sajjad, H., Dalvi, F., Durrani, N., Nakov, P.: Poor man’s BERT: smaller and faster transformer models. CoRR abs/2004.03844 (2020) · arXiv
- Sanh, V., Debut, L., Chaumond, J., Wolf, T.: DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. ArXiv abs/1910.01108 (2019) · arXiv
- Soares, I.B., Wei, D., Ramamurthy, K.N., Singh, M., Yurochkin, M.: Your fairness may vary: pretrained language model fairness in toxic text classification. In: Annual Meeting of the Association for Computational Linguistics (2022)
- Xu, J.M., Jun, K.S., Zhu, X., Bellmore, A.: Learning from bullying traces in social media. In: Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 656–666 (2012)
Cited by
Bias 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 1,245
Metadata
- Authors
- Proskurina, I., Metzler, G., & Velcin, J.
- Affiliations
- Université Lumière Lyon 2 (FR)
- Corresponding author
- Proskurina, Irina
- ORCID
- Velcin, Julien
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Switzerland
- Published
- 2023
- Volume
- 13876
- Pages
- 366-378
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4361982895
- Cited by
- 1
- References
- 16
- Citations by year
- 2024: 1
- FWCI
- 0.18
- Type
- conference paper
- Language
- en
- Open access
- green
- hal.science
- Licence
- cc-by
- www.springernature.com/gp/researchers/text-and-data-mining (tdm)
- www.springernature.com/gp/researchers/text-and-data-mining (vor)
- Locations
- Lecture notes in computer science (publishedVersion)
- HAL (submittedVersion, cc-by)
- Funders
- Agence Nationale de la Recherche (ANR-21-CE23-0026)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- model compression, algorithmic bias, hate speech detection, offensive language classification, gender bias, racial bias, religious bias, attention mechanism, BERT, RoBERTa, social media text, Twitter, Gab
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- CoChat. (2026). Citation check: The Other Side of Compression: Measuring Bias in Pruned Transformers. https://app.cochat.ai/papers/10.1007/978-3-031-30047-9_29
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- Proskurina, I., Metzler, G., & Velcin, J. (2023). The Other Side of Compression: Measuring Bias in Pruned Transformers. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-031-30047-9_29
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