From Natural Language to Exact Cover: A Neuro-Symbolic Approach to Zebra Puzzles· 从自然语言到精确覆盖：解决斑马谜题的神经符号方法
Chain-of-Thought (CoT) generation has substantially improved the performance of Large Language Models (LLMs) on complex reasoning tasks, including code generation, data analysis, and exam-style question answering. Despite these advances, purely neural LLMs continue to struggle with elementary logical reasoning problems and lack the determinism, soundness, and reliability characteristic of symbolic reasoning systems. Conversely, classical symbolic methods such as SAT solving and exact cover guarantee correctness and completeness, but require problems to be expressed in highly specialized formal encodings, limiting their applicability to natural language inputs. In this work, we present a synergistic neuro-symbolic framework that bridges this gap by combining neural semantic parsing with det
神经符号框架结合自然语言解析与精确覆盖算法解决逻辑谜题。
- 核心方法
- 使用神经语义解析将自然语言问题转换为精确覆盖问题的形式化表示，再通过符号推理求解。
- 适合谁读
- 研究者
- 要解决的问题
- 解决斑马谜题等逻辑推理问题时，纯神经模型缺乏符号系统的确定性和可靠性，而传统符号方法难以处理自然语言输入。
- 关键实验
- 未提供
- 主要贡献
- 提出了一个神经符号框架，能够处理自然语言输入并保证逻辑推理的正确性和完整性。
- 意义与局限
- 将神经模型和符号系统的优势结合，为解决复杂逻辑推理问题提供了一种新的方法。但可能受限于特定类型的逻辑问题。