用合成数据微调大模型，提升德语法律问答准确率
Domain-Adaptation through Synthetic Data: Fine-Tuning Large Language Models for German Law
- 从权威法规生成高质量法律问答对
- 微调后模型在德语法律任务上显著优于基线
- 适合法律、高精度知识领域模型优化
大型语言模型（LLMs）在法律推理等专业领域常因专家知识不足而产生事实错误或幻觉。本文提出一种新颖的合成数据生成方法，通过权威德国法律条文系统生成高质量、多样且法律准确的问答对，用于微调大模型。相比昂贵的人工标注或不可靠的合成方法，该方法结合严格的自动化过滤与参数高效微调技术，在德语法律问答任务中显著优于基线模型。结果表明，精心设计的合成数据可作为高风险、知识密集型领域中人工标注的可靠替代方案。
原文摘要 · Abstract (English)
Large language models (LLMs) often struggle in specialized domains such as legal reasoning due to limited expert knowledge, resulting in factually incorrect outputs or hallucinations. This paper presents an effective method for adapting advanced LLMs to German legal question answering through a novel synthetic data generation approach. In contrast to costly human-annotated resources or unreliable synthetic alternatives, our approach systematically produces high-quality, diverse, and legally accurate question-answer pairs directly from authoritative German statutes. Using rigorous automated filtering methods and parameter-efficient fine-tuning techniques, we demonstrate that LLMs adapted with our synthetic dataset significantly outperform their baseline counterparts on German legal question answering tasks. Our results highlight the feasibility of using carefully designed synthetic data as a robust alternative to manual annotation in high-stakes, knowledge-intensive domains.
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