用大模型自动学逻辑规则，让问答更准更可信。
Question Answering with LLMs and Learning from Answer Sets
- 大模型提取语义，自动转成可解释逻辑规则
- 在故事问答任务上准确率提升，优于人工规则
- 适合需要可解释推理的AI系统开发者
大语言模型擅长理解自然语言，但在明确的常识推理上表现不足。近期研究发现，结合大模型与强符号推理系统可解决这一问题，但现有方法依赖人工设计符号组件。本文提出LLM2LAS，融合大模型的自然语言理解、ILASP的规则学习能力及答案集编程（ASP）的形式化推理优势。大模型从文本中提取语义结构，ILASP将其转化为可解释的逻辑规则，再由ASP求解器进行精确一致的推理，从而正确回答未见问题。实验结果表明，该自动学习与推理方法在故事问答基准上展现出良好性能，同时揭示了其局限性。
原文摘要 · Abstract (English)
Large Language Models (LLMs) excel at understanding natural language but struggle with explicit commonsense reasoning. A recent trend of research suggests that the combination of LLM with robust symbolic reasoning systems can overcome this problem on story-based question answering tasks. In this setting, existing approaches typically depend on human expertise to manually craft the symbolic component. We argue, however, that this component can also be automatically learned from examples. In this work, we introduce LLM2LAS, a hybrid system that effectively combines the natural language understanding capabilities of LLMs, the rule induction power of the Learning from Answer Sets (LAS) system ILASP, and the formal reasoning strengths of Answer Set Programming (ASP). LLMs are used to extract semantic structures from text, which ILASP then transforms into interpretable logic rules. These rules allow an ASP solver to perform precise and consistent reasoning, enabling correct answers to previously unseen questions. Empirical results outline the strengths and weaknesses of our automatic approach for learning and reasoning in a story-based question answering benchmark.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。