DAGR: Decomposition Augmented Graph Retrieval with LLMs

• URL: http://arxiv.org/abs/2506.13380v3
• Date: Mon, 11 Aug 2025 10:35:31 GMT
• Title: DAGR: Decomposition Augmented Graph Retrieval with LLMs
• Authors: Valentin Six, Evan Dufraisse, Gaël de Chalendar,
• Abstract summary: DAGR is a retrieval method that leverages both complex questions and their decomposition in subquestions to extract relevant, linked subgraphs.<n>The resulting Graph-RAG pipeline is suited to handle complex multi-hop questions and effectively reason over graph-structured data.<n>We evaluate DAGR on standard multi-hop QA benchmarks and show that it achieves comparable or superior performance to competitive existing methods.
• Score: 1.034893617526558
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) excel at many Natural Language Processing (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 address this challenge, we introduce DAGR, a retrieval method that leverages both complex questions and their decomposition in subquestions to extract relevant, linked textual subgraphs. DAGR first breaks down complex queries, retrieves subgraphs guided by a weighted similarity function over both the original and decomposed queries, and creates a question-specific knowledge graph to guide answer generation. The resulting Graph-RAG pipeline is suited to handle complex multi-hop questions and effectively reason over graph-structured data. We evaluate DAGR 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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