用神经与符号结合方法，让大模型补全常识逻辑漏洞
A Balanced Neuro-Symbolic Approach for Commonsense Abductive Logic
- 用逻辑求解器反馈引导大模型逐步添加缺失的常识关系
- 在移除常识信息的数据集上，推理准确率显著提升
- 适合需要人类常识推理的复杂逻辑任务
尽管大型语言模型在形式推理上表现优异，但在需要复杂证明规划的问题上常失效。一种改进方法是将问题转化为形式逻辑并使用逻辑求解器。然而，现有求解器假设所有相关事实均已给出，无法处理缺失的常识关系。本文提出一种新方法：通过逻辑求解器的反馈，迭代地由大模型补充缺失的常识关系，同时设计搜索策略以在可接受成本下最大化发现有效事实的概率。在一组移除了部分常识信息的纯逻辑推理数据集上，该方法持续优于现有技术，验证了神经与符号元素平衡在人类情境推理中的价值。
原文摘要 · Abstract (English)
Although Large Language Models (LLMs) have demonstrated impressive formal reasoning abilities, they often break down when problems require complex proof planning. One promising approach for improving LLM reasoning abilities involves translating problems into formal logic and using a logic solver. Although off-the-shelf logic solvers are in principle substantially more efficient than LLMs at logical reasoning, they assume that all relevant facts are provided in a question and are unable to deal with missing commonsense relations. In this work, we propose a novel method that uses feedback from the logic solver to augment a logic problem with commonsense relations provided by the LLM, in an iterative manner. This involves a search procedure through potential commonsense assumptions to maximize the chance of finding useful facts while keeping cost tractable. On a collection of pure-logical reasoning datasets, from which some commonsense information has been removed, our method consistently achieves considerable improvements over existing techniques, demonstrating the value in balancing neural and symbolic elements when working in human contexts.
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