弥合法律解释与形式逻辑：忠实性、假设与AI法律推理的未来
Bridging Legal Interpretation and Formal Logic: Faithfulness, Assumption, and the Future of AI Legal Reasoning
AI总结：
本文提出一种结合大语言模型与形式验证的神经符号方法，旨在提升AI辅助法律推理的可靠性与可信度，减少人工验证负担。
摘要：
大型语言模型在法律实践中日益普及，带来了巨大潜力和严重风险。法律专业人士可能受益于能推理合同、起草文件和分析来源的AI，但法律工作的高风险性要求更高的严谨性，而当前AI系统无法满足。核心问题不仅是LLM编造事实和引用，而是系统性地推导出超出源文本支持的结论，将充满假设的结论视为逻辑严谨。本文提出一种神经符号方法，结合大语言模型的表达力与形式验证的严谨性，旨在使AI辅助的法律推理既具备能力又值得信赖，从而减少人工验证的负担，而不牺牲法律实践所要求的责任性。
英文摘要：
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.
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。
