Event Grounded Criminal Court View Generation with Cooperative (Large) Language Models

• URL: http://arxiv.org/abs/2404.07001v3
• Date: Tue, 16 Apr 2024 06:34:31 GMT
• Title: Event Grounded Criminal Court View Generation with Cooperative (Large) Language Models
• Authors: Linan Yue, Qi Liu, Lili Zhao, Li Wang, Weibo Gao, Yanqing An,
• Abstract summary: We propose an Event Grounded Generation (EGG) method for criminal court view generation with cooperative (Large) Language Models. We first design a LLMs-based extraction method that can extract events in case facts without massive annotated events. Then, we incorporate the extracted events into court view generation by merging case facts and events.
• Score: 12.569076604769156
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the development of legal intelligence, Criminal Court View Generation has attracted much attention as a crucial task of legal intelligence, which aims to generate concise and coherent texts that summarize case facts and provide explanations for verdicts. Existing researches explore the key information in case facts to yield the court views. Most of them employ a coarse-grained approach that partitions the facts into broad segments (e.g., verdict-related sentences) to make predictions. However, this approach fails to capture the complex details present in the case facts, such as various criminal elements and legal events. To this end, in this paper, we propose an Event Grounded Generation (EGG) method for criminal court view generation with cooperative (Large) Language Models, which introduces the fine-grained event information into the generation. Specifically, we first design a LLMs-based extraction method that can extract events in case facts without massive annotated events. Then, we incorporate the extracted events into court view generation by merging case facts and events. Besides, considering the computational burden posed by the use of LLMs in the extraction phase of EGG, we propose a LLMs-free EGG method that can eliminate the requirement for event extraction using LLMs in the inference phase. Extensive experimental results on a real-world dataset clearly validate the effectiveness of our proposed method.

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