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Fairlex: A multilingual benchmark for evaluating fairness in legal text processing ...
Abstract: We present a benchmark suite of four datasets for evaluating the fairness of pre-trained legal language models and the techniques used to fine-tune them for downstream tasks. Our benchmarks cover four jurisdictions (European Council, USA, Swiss, and Chinese), five languages (English, German, French, Italian, and Chinese), and fairness across five attributes (gender, age, nationality/region, language, and legal area). In our experiments, we evaluate pre-trained language models using several group-robust fine-tuning techniques and show that performance group disparities are vibrant in many cases, while none of these techniques guarantee fairness, nor consistently mitigate group disparities. Furthermore, we provide a quantitative and qualitative analysis of our results, highlighting open challenges in the development of robustness methods in legal NLP. ...
Keyword: fairlex; fairness; legal
URL: https://dx.doi.org/10.5281/zenodo.6322643
https://zenodo.org/record/6322643
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Fairlex: A multilingual benchmark for evaluating fairness in legal text processing ...
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3
UK-LEX Dataset - Part of Chalkidis and Søgaard (2022) ...
Chalkidis, Ilias; Søgaard, Anders. - : Zenodo, 2022
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4
UK-LEX Dataset - Part of Chalkidis and Søgaard (2022) ...
Chalkidis, Ilias; Søgaard, Anders. - : Zenodo, 2022
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5
FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing ...
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6
Generalized Quantifiers as a Source of Error in Multilingual NLU Benchmarks ...
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7
Challenges and Strategies in Cross-Cultural NLP ...
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8
Factual Consistency of Multilingual Pretrained Language Models ...
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9
Zero-Shot Dependency Parsing with Worst-Case Aware Automated Curriculum Learning ...
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10
How Conservative are Language Models? Adapting to the Introduction of Gender-Neutral Pronouns ...
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