通过提取关系图并追踪推理路径，提升大模型在复杂关系推理中的准确性和鲁棒性。
Extracting and Following Paths for Robust Relational Reasoning with Large Language Models
- 将关系推理分解为图构建、路径识别和推理三阶段。
- 在4个数据集上最高提升21.3%，且无需微调或大量调用模型。
- 利用图结构的组合特性增强对模型错误和输入模糊的容忍度。
大型语言模型（LLMs）具备丰富的语义知识，但在亲属关系或空间关系等复杂推理任务中表现不佳。本文提出路径思维（PoT）框架，将关系推理任务分解为三个关键阶段：图提取、路径识别和推理。与以往方法不同，PoT高效地从上下文中提取包含关键实体、关系和属性的推理图，并在图中识别与查询相关的推理路径，以支持后续答案推导。在四个关系推理数据集上的实验表明，PoT显著优于现有最先进基线（最高提升21.3%），且无需微调或大量调用大模型。此外，与以往神经符号方法相比，PoT通过图结构的组合特性，增强了对大模型提取错误和输入模糊的鲁棒性。
原文摘要 · Abstract (English)
Large language models (LLMs) possess vast semantic knowledge but often struggle with complex reasoning tasks, particularly in relational reasoning problems such as kinship or spatial reasoning. In this paper, we present Path-of-Thoughts (PoT), a novel framework for solving relation reasoning that decomposes the task into three key stages: graph extraction, path identification, and reasoning. Unlike previous approaches, PoT efficiently extracts a reasoning graph that identifies crucial entities, relations, and attributes within the context. Subsequently, PoT identifies query-relevant reasoning paths within the graph, facilitating downstream reasoning of potential answers. Experimental evaluations across four datasets of relational reasoning demonstrate that PoT surpasses state-of-the-art baselines by a significant margin (up to 21.3%) without requiring fine-tuning or extensive LLM calls. Furthermore, unlike prior neuro-symbolic methods, PoT exhibits improved resilience against LLM extraction errors and input ambiguity by leveraging the compositional nature of graphs.
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