LEXTREME: A Multi-Lingual and Multi-Task Benchmark for the Legal Domain
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Abstract (arXiv)
Lately, propelled by the phenomenal advances around the transformer architecture, the legal NLP field has enjoyed spectacular growth. To measure progress, well curated and challenging benchmarks are crucial. However, most benchmarks are English only and in legal NLP specifically there is no multilingual benchmark available yet. Additionally, many benchmarks are saturated, with the best models clearly outperforming the best humans and achieving near perfect scores. We survey the legal NLP literature and select 11 datasets covering 24 languages, creating LEXTREME. To provide a fair comparison, we propose two aggregate scores, one based on the datasets and one on the languages. The best baseline (XLM-R large) achieves both a dataset aggregate score a language aggregate score of 61.3. This indicates that LEXTREME is still very challenging and leaves ample room for improvement. To make it easy for researchers and practitioners to use, we release LEXTREME on huggingface together with all the code required to evaluate models and a public Weights and Biases project with all the runs.
Key facts. Cited by 68 papers in the map (data through 7 Oct 2026). Builds on 60 of its 112 references that are in the map. First posted on arXiv 30 Jan 2023.
Builds on
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References · 60
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50 most cited of 60 All 60 (JSON)
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Citing papers per year: 2023: 8; 2024: 19; 2025: 14; 2026: 27.
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- 2026InsufficiencyBench: Evaluating LLM legal advice on underspecified user queries20 Aug 2026
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