LexChronos：一个用于印度法理学中结构化事件时间线提取的代理框架
LexChronos: An Agentic Framework for Structured Event Timeline Extraction in Indian Jurisprudence
AI总结：
LexChronos通过双代理架构提取印度最高法院判决中的结构化事件时间线，提升法律文本理解和推理能力。
摘要：
理解并预测司法结果需要对法律文件进行细致的分析。传统方法将判决和程序视为无结构的文本，限制了大型语言模型（LLMs）在摘要、论点生成和判决预测等任务中的有效性。我们提出了LexChronos，一个代理框架，能够迭代地从印度最高法院的判决中提取结构化事件时间线。LexChronos采用双代理架构：一个经过LoRA-instruct微调的提取代理识别候选事件，而一个预训练的反馈代理通过置信度驱动的循环对它们进行评分和优化。为了解决印度法律事件数据集稀缺的问题，我们使用DeepSeek-R1和GPT-4的反向工程技术构建了一个包含2000个样本的合成语料库，生成了黄金标准的事件注释。我们的流程在该合成地面真相上实现了基于BERT的F1分数为0.8751。在下游评估中，GPT-4在75%的情况下更倾向于使用结构化时间线而非无结构的基线，展示了在印度法理学中改进的理解和推理能力。这项工作通过利用法律事件的结构化表示，为未来在印度背景下应用的法律人工智能应用奠定了基础，如先例映射、论点综合和预测判决建模。
英文摘要：
Understanding and predicting judicial outcomes demands nuanced analysis of legal documents. Traditional approaches treat judgments and proceedings as unstructured text, limiting the effectiveness of large language models (LLMs) in tasks such as summarization, argument generation, and judgment prediction. We propose LexChronos, an agentic framework that iteratively extracts structured event timelines from Supreme Court of India judgments. LexChronos employs a dual-agent architecture: a LoRA-instruct-tuned extraction agent identifies candidate events, while a pre-trained feedback agent scores and refines them through a confidence-driven loop. To address the scarcity of Indian legal event datasets, we construct a synthetic corpus of 2000 samples using reverse-engineering techniques with DeepSeek-R1 and GPT-4, generating gold-standard event annotations. Our pipeline achieves a BERT-based F1 score of 0.8751 against this synthetic ground truth. In downstream evaluations on legal text summarization, GPT-4 preferred structured timelines over unstructured baselines in 75% of cases, demonstrating improved comprehension and reasoning in Indian jurisprudence. This work lays a foundation for future legal AI applications in the Indian context, such as precedent mapping, argument synthesis, and predictive judgment modelling, by harnessing structured representations of legal events.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
