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.

翻译：领域特定实体识别在法律语境中具有显著重要性，作为一项基础性任务，它支撑着针对判例法文档的多种应用，包括问答系统、文本摘要、机器翻译、情感分析和信息检索。最近的进展凸显了大型语言模型在自然语言处理任务中的有效性，证明了其能够从临床和金融文档等专业文本中准确检测和分类领域特定事实（实体）。本研究探讨了大型语言模型在判例法文档中识别领域特定实体（例如，法院、请愿人、法官、律师、被申请人、FIR 编号等）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。该研究评估了包括 Large Language Model Meta AI 3、Mistral 和 Gemma 在内的最先进大型语言模型架构，在抽取适用于印度司法文本的司法事实方面的性能。Mistral 和 Gemma 成为表现最佳的模型，展现了对于准确实体识别至关重要的均衡精确率和召回率。这些发现证实了大型语言模型在司法文档中的价值，并展示了它们如何通过生成适用于深入分析的精确、结构化数据输出，来促进和加速科学研究。