关键词: 大语言模型, 动态提示, 裁判文书摘要, 结构缺失, 事实不一致

Abstract:  In view of the problems of complex case structure, redundant facts involved, and wide distribution of cases in judicial documents, the existing large language model was difficult to effectively focus on structural information and factual errors, resulting in missing structural information and inconsistent facts. To this end, a judicial document summary method combining a large language model and dynamic prompts, DPCM (Dynamic Prompt Correction Method), was proposed. First, a large language model was used for single-sample learning to generate judicial document summaries. Secondly, the high-dimensional similarity between the original text and the summary was calculated to detect possible structural missing or factual inconsistency problems in the summary. If a problem was found, the wrong summary was spliced with the original text, and the prompt word was added. Then, single-sample learning was performed again to correct and generate a new summary, and a similarity test was performed. If the problem still existed, the generation and detection process was repeated. Finally, through this iterative method, the prompt word was dynamically adjusted to gradually optimize the generated summary. The experimental results on the CAIL2021 public justice summary dataset showed that compared with Least-To-Most Prompting, Zero-Shot Reasoners, Self-Consistency CoT and other methods, the proposed method showed improvements in Rouge-1, Rouge-2, Rouge-L, BERTscore, FactCC (F1), and FactCC (Acc) indicators.

Key words: large language model, dynamic prompt, judgment document summary, lack of structure, factual inconsistency