从判决到问题：具有引用幻觉控制的法律推理结构化提取
From Judgments to Issues: Structured Extraction of Legal Reasoning with Citation-Hallucination Control
AI总结：
提出自动化流程，将意大利税务法庭判决分解为法律问题，基于IRAC框架和法律三段论提取结构化XML表示，用通用模型并结合幻觉检测过滤器处理大量判决，经专家验证，为下游应用提供起点。
摘要：
我们提出了一个自动化管道，将意大利税务法庭的判决分解为各个法律问题，并为每个问题提取基于IRAC框架和法律三段论的结构化XML表示。该管道针对约33万个意大利税务法庭的一审和二审判决语料库构建，围绕一个功能强大但成本效益高的通用模型（DeepSeek V3）构建，该选择是出于以可持续成本处理数十万份文档的需要。为了解决大语言模型在法律引用方面有据可查的不可靠性，我们在提取步骤中加入了一个自动幻觉检测过滤器，该过滤器将模型生成的参考文献与由专用解析器（Linkoln）在判决文本中识别的参考文献进行比较，并将其标准化为标准标识符（URN-NIR、ECLI、CELEX）。我们在由两名税法博士注释的50份判决上验证了该管道，计算了问题提取和法律引用方面的注释者间一致性和大语言模型与专家的一致性，以及幻觉过滤器的独立评估。据我们所知，这是第一个针对意大利税务法庭判决的具有幻觉控制的问题级、专家验证的结构化提取管道，它为下游应用（如问题级检索、引用网络分析和大规模法律推理数据集的构建）提供了一个具体的起点。
英文摘要：
We present an automated pipeline that decomposes Italian tax-court judgments into individual legal issues and extracts, for each issue, a structured XML representation grounded in the IRAC framework and the legal syllogism. The pipeline targets a corpus of approximately $330{,}000$ first- and second-instance decisions of the Italian tax courts and is built around a capable yet cost-efficient general-purpose model (DeepSeek V3), a choice driven by the need to process several hundred thousand documents at a sustainable cost. To address the well-documented unreliability of large language models on legal citations, we couple the extraction step with an automatic hallucination-detection filter that compares the references produced by the model with those identified in the judgment text by a dedicated parser (Linkoln), normalised to standard identifiers (URN-NIR, ECLI, CELEX). We validate the pipeline on $50$ judgments annotated by two PhDs in tax law, computing inter-annotator agreement and LLM-vs-expert agreement on both issue extraction and legal citations, together with a stand-alone evaluation of the hallucination filter. To the best of our knowledge, this is the first issue-level, expert-validated structured extraction pipeline with hallucination control for Italian tax-court decisions, and it provides a concrete starting point for downstream applications such as issue-level retrieval, citation-network analysis, and the construction of large-scale datasets of legal reasoning.
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。
