Decompositional Reasoning for Graph Retrieval with Large Language Models

• URL: http://arxiv.org/abs/2506.13380v1
• Date: Mon, 16 Jun 2025 11:44:28 GMT
• Title: Decompositional Reasoning for Graph Retrieval with Large Language Models
• Authors: Valentin Six, Evan Dufraisse, Gaël de Chalendar,
• Abstract summary: Large Language Models (LLMs) excel at many NLP tasks, but struggle with multi-hop reasoning and factual consistency.<n>We propose a novel retrieval approach that integrates textual knowledge graphs into the LLM reasoning process via query decomposition.<n>Our method decomposes complex questions into sub-questions, retrieves relevant textual subgraphs, and composes a question-specific knowledge graph to guide answer generation.
• Score: 1.034893617526558
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) excel at many NLP tasks, but struggle with multi-hop reasoning and factual consistency, limiting their effectiveness on knowledge-intensive tasks like complex question answering (QA). Linking Knowledge Graphs (KG) and LLMs has shown promising results, but LLMs generally lack the ability to reason efficiently over graph-structured information. To tackle this problem, we propose a novel retrieval approach that integrates textual knowledge graphs into the LLM reasoning process via query decomposition. Our method decomposes complex questions into sub-questions, retrieves relevant textual subgraphs, and composes a question-specific knowledge graph to guide answer generation. For that, we use a weighted similarity function that focuses on both the complex question and the generated subquestions to extract a relevant subgraph, which allows efficient and precise retrieval for complex questions and improves the performance of LLMs on multi-hop QA tasks. This structured reasoning pipeline enhances factual grounding and interpretability while leveraging the generative strengths of LLMs. We evaluate our method on standard multi-hop QA benchmarks and show that it achieves comparable or superior performance to competitive existing methods, using smaller models and fewer LLM calls.

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