In this section
@inproceedings{Lindsay:2025:10.3233/FAIA251582,
author = {Lindsay, C and Toni, F and Nguyen, TD and Nguyen, TM and Pham, NAT and Chu, QH and Nguyen, HN and Nguyen, LM},
doi = {10.3233/FAIA251582},
pages = {121--132},
title = {Argumentative LLMs for Legal Information Entailment},
url = {http://dx.doi.org/10.3233/FAIA251582},
year = {2025}
}
TY - CPAPER
AB - If legal professionals are to use Large Language Models (LLMs), LLMs must provide explanations to empower safe decision making. Argumentative LLMs (ArgLLMs) were recently introduced to produce and explain decisions in the form of claim verifications; they provide explanations that faithfully match their underlying reasoning and are contestable by human users. In this paper, ArgLLMs are applied to two COLIEE 2025 legal entailment tasks: task 4, which involves determining whether relevant statute articles entail a legal hypothesis, and the pilot task, which involves determining whether tort case information entails the conclusion that the tort case was affirmed by the judge. We perform an ablation study to assess how several modifications to ArgLLMs affect accuracy in the two tasks. We also compare the performance of our variants of ArgLLMs against two baselines, one involving a single zero-shot Chain of Thought (CoT) prompt and another involving a pairwise comparison of supporting and attacking arguments. Our experiments show that ArgLLMs are more accurate than over half of all official submissions to task 4 and the pilot task of the COLIEE competition. Moreover, Arg-LLMs produce accuracy scores similar to the CoT baseline, while also providing the benefit of faithful and contestable explanations behind the decision made. For task 4, we also conducted a pilot study where legal experts reviewed 20 generated explanations and found the arguments on the correct side of the debate to be both sound and faithful to the legal context provided in 17 of them. Repository link: https://github.com/charlieblindsay/arg-llms
AU - Lindsay,C
AU - Toni,F
AU - Nguyen,TD
AU - Nguyen,TM
AU - Pham,NAT
AU - Chu,QH
AU - Nguyen,HN
AU - Nguyen,LM
DO - 10.3233/FAIA251582
EP - 132
PY - 2025///
SN - 0922-6389
SP - 121
TI - Argumentative LLMs for Legal Information Entailment
UR - http://dx.doi.org/10.3233/FAIA251582
ER -