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Summary of Lar-echr: a New Legal Argument Reasoning Task and Dataset For Cases Of the European Court Of Human Rights, by Odysseas S. Chlapanis et al.


by Odysseas S. Chlapanis, Dimitrios Galanis, Ion Androutsopoulos

First submitted to arxiv on: 17 Oct 2024

Categories

  • Main: Computation and Language (cs.CL)
  • Secondary: Artificial Intelligence (cs.AI)

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Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
The novel Legal Argument Reasoning (LAR) task is designed to evaluate the legal reasoning capabilities of Large Language Models (LLMs). LAR requires selecting the correct next statement in a chain of legal arguments from court proceedings, given the facts of the case. To evaluate seven general-purpose LLMs on this task, we constructed a dataset (LAR-ECHR) using cases from the European Court of Human Rights (ECHR). Our results show that even though LAR-ECHR is based on EU law, its ranking of models aligns with that of LegalBench, an established US-based legal reasoning benchmark. We also found that LAR-ECHR distinguishes top models more clearly than LegalBench and that the best model (GPT-4o) achieves 75.8% accuracy on this task. These findings indicate significant potential for further model improvement.
Low GrooveSquid.com (original content) Low Difficulty Summary
Legal Argument Reasoning is a new way to test how well computer programs can understand and make decisions based on legal arguments. We used court cases from the European Court of Human Rights to create a special set of examples that challenge these programs to pick the next step in a logical argument. The results show that some programs are better at this task than others, even when they’re not familiar with EU law. This means that we can use this test to make these programs even better at understanding and working with legal arguments.

Keywords

» Artificial intelligence  » Gpt