Domain-specific Entity Recognition holds significant importance in legal contexts, serving as a fundamental task that supports various applications such as question-answering systems, text summarization, machine translation, sentiment analysis, and information retrieval specifically within case law documents. Recent advancements have highlighted the efficacy of Large Language Models in natural language processing tasks, demonstrating their capability to accurately detect and classify domain-specific facts (entities) from specialized texts like clinical and financial documents. This research investigates the application of Large Language Models in identifying domain-specific entities (e.g., courts, petitioner, judge, lawyer, respondents, FIR nos.) within case law documents, with a specific focus on their aptitude for handling domain-specific language complexity and contextual variations. The study evaluates the performance of state-of-the-art Large Language Model architectures, including Large Language Model Meta AI 3, Mistral, and Gemma, in the context of extracting judicial facts tailored to Indian judicial texts. Mistral and Gemma emerged as the top-performing models, showcasing balanced precision and recall crucial for accurate entity identification. These findings confirm the value of Large Language Models in judicial documents and demonstrate how they can facilitate and quicken scientific research by producing precise, organised data outputs that are appropriate for in-depth examination.

此研究探讨了大型语言模型在识别司法文件中的领域特定实体的应用，特别关注其处理领域特定语言复杂性和上下文变化的能力，并评估了包括Large Language Model Meta AI 3、Mistral和Gemma在内的最先进大型语言模型架构在提取适用于印度司法文本的司法事实方面的性能，其中Mistral和Gemma表现出优异的精确度和召回率，证实了大型语言模型在司法文件中的价值，展示了它们如何通过生成精确、有组织的数据输出来促进和加快科学研究，适合进行深入的研究。

大型语言模型在司法实体抽取中的比较研究