@inproceedings{deng-etal-2023-syllogistic,
title = "Syllogistic Reasoning for Legal Judgment Analysis",
author = "Deng, Wentao and
Pei, Jiahuan and
Kong, Keyi and
Chen, Zhe and
Wei, Furu and
Li, Yujun and
Ren, Zhaochun and
Chen, Zhumin and
Ren, Pengjie",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.864",
doi = "10.18653/v1/2023.emnlp-main.864",
pages = "13997--14009",
abstract = "Legal judgment assistants are developing fast due to impressive progress of large language models (LLMs). However, people can hardly trust the results generated by a model without reliable analysis of legal judgement. For legal practitioners, it is common practice to utilize syllogistic reasoning to select and evaluate the arguments of the parties as part of the legal decision-making process. But the development of syllogistic reasoning for legal judgment analysis is hindered by the lack of resources: (1) there is no large-scale syllogistic reasoning dataset for legal judgment analysis, and (2) there is no set of established benchmarks for legal judgment analysis. In this paper, we construct and manually correct a syllogistic reasoning dataset for legal judgment analysis. The dataset contains 11,239 criminal cases which cover 4 criminal elements, 80 charges and 124 articles. We also select a set of large language models as benchmarks, and conduct a in-depth analysis of the capacity of their legal judgment analysis.",
}
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<abstract>Legal judgment assistants are developing fast due to impressive progress of large language models (LLMs). However, people can hardly trust the results generated by a model without reliable analysis of legal judgement. For legal practitioners, it is common practice to utilize syllogistic reasoning to select and evaluate the arguments of the parties as part of the legal decision-making process. But the development of syllogistic reasoning for legal judgment analysis is hindered by the lack of resources: (1) there is no large-scale syllogistic reasoning dataset for legal judgment analysis, and (2) there is no set of established benchmarks for legal judgment analysis. In this paper, we construct and manually correct a syllogistic reasoning dataset for legal judgment analysis. The dataset contains 11,239 criminal cases which cover 4 criminal elements, 80 charges and 124 articles. We also select a set of large language models as benchmarks, and conduct a in-depth analysis of the capacity of their legal judgment analysis.</abstract>
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%0 Conference Proceedings
%T Syllogistic Reasoning for Legal Judgment Analysis
%A Deng, Wentao
%A Pei, Jiahuan
%A Kong, Keyi
%A Chen, Zhe
%A Wei, Furu
%A Li, Yujun
%A Ren, Zhaochun
%A Chen, Zhumin
%A Ren, Pengjie
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F deng-etal-2023-syllogistic
%X Legal judgment assistants are developing fast due to impressive progress of large language models (LLMs). However, people can hardly trust the results generated by a model without reliable analysis of legal judgement. For legal practitioners, it is common practice to utilize syllogistic reasoning to select and evaluate the arguments of the parties as part of the legal decision-making process. But the development of syllogistic reasoning for legal judgment analysis is hindered by the lack of resources: (1) there is no large-scale syllogistic reasoning dataset for legal judgment analysis, and (2) there is no set of established benchmarks for legal judgment analysis. In this paper, we construct and manually correct a syllogistic reasoning dataset for legal judgment analysis. The dataset contains 11,239 criminal cases which cover 4 criminal elements, 80 charges and 124 articles. We also select a set of large language models as benchmarks, and conduct a in-depth analysis of the capacity of their legal judgment analysis.
%R 10.18653/v1/2023.emnlp-main.864
%U https://aclanthology.org/2023.emnlp-main.864
%U https://doi.org/10.18653/v1/2023.emnlp-main.864
%P 13997-14009
Markdown (Informal)
[Syllogistic Reasoning for Legal Judgment Analysis](https://aclanthology.org/2023.emnlp-main.864) (Deng et al., EMNLP 2023)
ACL
- Wentao Deng, Jiahuan Pei, Keyi Kong, Zhe Chen, Furu Wei, Yujun Li, Zhaochun Ren, Zhumin Chen, and Pengjie Ren. 2023. Syllogistic Reasoning for Legal Judgment Analysis. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 13997–14009, Singapore. Association for Computational Linguistics.