Xie, Z;
Kocijan, V;
Lukasiewicz, T;
Camburu, OM;
(2023)
Counter-GAP: Counterfactual Bias Evaluation through Gendered Ambiguous Pronouns.
In: Vlachos, Adreas and Augenstein, Isabelle, (eds.)
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics.
(pp. p. 3761).
Association for Computational Linguistics (ACL)
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Abstract
Bias-measuring datasets play a critical role in detecting biased behavior of language models and in evaluating progress of bias mitigation methods. In this work, we focus on evaluating gender bias through coreference resolution, where previous datasets are either hand-crafted or fail to reliably measure an explicitly defined bias. To overcome these shortcomings, we propose a novel method to collect diverse, natural, and minimally distant text pairs via counterfactual generation, and construct Counter-GAP, an annotated dataset consisting of 4008 instances grouped into 1002 quadruples. We further identify a bias cancellation problem in previous group-level metrics on Counter-GAP, and propose to use the difference between inconsistency across genders and within genders to measure bias at a quadruple level. Our results show that four pre-trained language models are significantly more inconsistent across different gender groups than within each group, and that a name-based counterfactual data augmentation method is more effective to mitigate such bias than an anonymization-based method.
Type: | Proceedings paper |
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Title: | Counter-GAP: Counterfactual Bias Evaluation through Gendered Ambiguous Pronouns |
Event: | 17th Conference of the European Chapter of the Association for Computational Linguistics |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.18653/v1/2023.eacl-main.272 |
Publisher version: | http://dx.doi.org/10.18653/v1/2023.eacl-main.272 |
Language: | English |
Additional information: | © 2023 ACL. Permission is granted to make copies for the purposes of teaching and research. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/). |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/10172075 |
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