用大模型生成规则框架，再用逻辑编程精确求解，提升小样本逻辑推理效率。
Abductive Logical Rule Induction by Bridging Inductive Logic Programming and Multimodal Large Language Models
- 借助大模型提出结构正确的规则草案，降低逻辑搜索空间。
- 在多个基准上超越纯逻辑或纯大模型方法，准确率提升12.7%。
- 适合需要精准逻辑推理的生成任务，如图文定制化创作。
我们提出ILP-CoT，将归纳逻辑编程（ILP）与多模态大语言模型（MLLMs）结合，用于归纳性逻辑规则推导。该任务需从少量非结构化文本或视觉输入中发现逻辑事实并推导规则，传统ILP因需预设背景知识和计算成本高而受限，纯MLLM则易出现感知幻觉。基于观察：即使存在幻觉，MLLM仍能提出结构正确的规则，我们自动构建带剪枝搜索空间的ILP任务，利用ILP系统输出基于修正逻辑事实与形式归纳推理的结果。在多个挑战性逻辑推导基准上验证有效性，并展示其在文本到图像个性化生成中的应用潜力。代码与数据已开源。
原文摘要 · Abstract (English)
We propose ILP-CoT, a method that bridges Inductive Logic Programming (ILP) and Multimodal Large Language Models (MLLMs) for abductive logical rule induction. The task involves both discovering logical facts and inducing logical rules from a small number of unstructured textual or visual inputs, which still remain challenging when solely relying on ILP, due to the requirement of specified background knowledge and high computational cost, or MLLMs, due to the appearance of perceptual hallucinations. Based on the key observation that MLLMs could propose structure-correct rules even under hallucinations, our approach automatically builds ILP tasks with pruned search spaces based on the rule structure proposals from MLLMs, and utilizes ILP system to output rules built upon rectified logical facts and formal inductive reasoning. Its effectiveness is verified through challenging logical induction benchmarks, as well as a potential application of our approach, namely text-to-image customized generation with rule induction. Our code and data are released at https://github.com/future-item/ILP-CoT.
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