用大模型自动构建逻辑编程的搜索规则，让机器自己找规律。
Hypothesis Generation via LLM-Automated Language Bias for ILP
- 多智能体大模型从文本生成逻辑规则的初始框架
- 在复杂场景中比传统方法更准确地发现隐藏规律
- 适合需要可解释性推理的研究者和工业应用
归纳逻辑编程（ILP）是一种从数据中提炼规律并生成可解释逻辑程序的严谨方法。但其关键瓶颈在于依赖专家手动设计语言偏差——即谓词库、类型和模式声明，这些定义了搜索空间。本文提出基于大模型自动构建语言偏差的假设生成方法：多智能体大模型从原始文本中设计偏差，并将描述转化为带类型的事实；随后由鲁棒的ILP求解器在全局一致性目标下推导规则。该方法降低了对预设符号结构的依赖，也克服了仅用大模型直接生成假设时对噪声敏感的问题。在多种挑战性场景下的大量实验验证了其优越性能，为假设生成提供了一条实用、可解释且可验证的新路径。
原文摘要 · Abstract (English)
Inductive Logic Programming (ILP) is a principled approach for generalizing regularities from data and constructing hypotheses as interpretable logic programs. However, a key limitation is its reliance on expert-crafted language bias - the predicate inventory, types, and mode declarations that delimit the search space. We propose hypothesis generation via LLM-automated language bias: multi-agent LLMs design the bias from raw text and translate descriptions into typed facts, and a robust ILP solver induces rules under a global consistency objective. This approach reduces traditional ILP's reliance on predefined symbolic structures and the noise sensitivity of LLM-only pipelines that directly generate hypotheses as text or code. Extensive experiments in diverse, challenging scenarios validate superior performance, providing a practical, explainable, and verifiable route to hypothesis generation.
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