用神经符号方法让AI法律推理更可信，避免无依据推断。
Bridging Legal Interpretation and Formal Logic: Faithfulness, Assumption, and the Future of AI Legal Reasoning
- 结合大模型与形式逻辑，实现可验证的法律推理
- 解决当前AI推理过度依赖假设、缺乏事实支撑的问题
- 适合需要高可靠性法律AI的律师与司法机构
大型语言模型在法律实践中的应用日益广泛，虽能协助合同分析、文书起草和大规模文献审查，但其高风险性不容忽视。当前核心问题并非仅限于模型虚构事实或引用错误，更在于它们会系统性地做出超出原文支持范围的推论，将充满假设的结论伪装成逻辑严谨的判断。本文提出一种神经符号方法，融合大模型的表达能力与形式化验证的严谨性，旨在使AI辅助法律推理既具备能力又值得信赖，从而在不增加人工核查负担的前提下，满足法律职业对问责性的严格要求。
原文摘要 · Abstract (English)
The growing adoption of large language models in legal practice brings both significant promise and serious risk. Legal professionals stand to benefit from AI that can reason over contracts, draft documents, and analyze sources at scale, yet the high-stakes nature of legal work demands a level of rigor that current AI systems do not provide. The central problem is not simply that LLMs hallucinate facts and references; it is that they systematically draw inferences that go beyond what the source text actually supports, presenting assumption-laden conclusions as if they were logically grounded. This proposal presents a neuro-symbolic approach to legal AI that combines the expressive power of large language models with the rigor of formal verification, aiming to make AI-assisted legal reasoning both capable and trustworthy, thus reducing the burden of manual verification without sacrificing the accountability that legal practice demands.
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