LexGLUE: A Benchmark Dataset for Legal Language Understanding in English
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Cited by 28 in LLM reasoning, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
Laws and their interpretations, legal arguments and agreements are typically expressed in writing, leading to the production of vast corpora of legal text. Their analysis, which is at the center of legal practice, becomes increasingly elaborate as these collections grow in size. Natural language understanding (NLU) technologies can be a valuable tool to support legal practitioners in these endeavors. Their usefulness, however, largely depends on whether current state-of-the-art models can generalize across various tasks in the legal domain. To answer this currently open question, we introduce the Legal General Language Understanding Evaluation (LexGLUE) benchmark, a collection of datasets for evaluating model performance across a diverse set of legal NLU tasks in a standardized way. We also provide an evaluation and analysis of several generic and legal-oriented models demonstrating that the latter consistently offer performance improvements across multiple tasks.
Key facts. Cited by 310 papers in the map (data through 14 Oct 2026). Builds on 50 of its 118 references that are in the map. First posted on arXiv 3 Oct 2021.
Builds on
50 of 118 references.
References · 50
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Citing papers per year: 2022: 31; 2023: 50; 2024: 56; 2025: 69; 2026: 104.
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