%0 Conference Proceedings
%T Can Large Language Models Grasp Legal Theories? Enhance Legal Reasoning with Insights from Multi-Agent Collaboration
%A Yuan, Weikang
%A Cao, Junjie
%A Jiang, Zhuoren
%A Kang, Yangyang
%A Lin, Jun
%A Song, Kaisong
%A Lin, Tianqianjin
%A Yan, Pengwei
%A Sun, Changlong
%A Liu, Xiaozhong
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F yuan-etal-2024-large
%R 10.18653/v1/2024.findings-emnlp.445
%U https://aclanthology.org/2024.findings-emnlp.445/
%U https://doi.org/10.18653/v1/2024.findings-emnlp.445
%P 7577-7597