CC-RAG: Structured Multi-Hop Reasoning via Theme-Based Causal Graphs

• URL: http://arxiv.org/abs/2506.08364v2
• Date: Wed, 11 Jun 2025 01:49:12 GMT
• Title: CC-RAG: Structured Multi-Hop Reasoning via Theme-Based Causal Graphs
• Authors: Jash Rajesh Parekh, Pengcheng Jiang, Jiawei Han,
• Abstract summary: Causal-Chain RAG (CC-RAG) is a novel approach that integrates zero-shot triple extraction and theme-aware graph chaining into the RAG pipeline.<n>Given a domain specific corpus, CC-RAG constructs a Directed Acyclic Graph (DAG) of cause, relation, effect> triples and uses forward/backward chaining to guide structured answer generation.
• Score: 23.587337743113228
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding cause and effect relationships remains a formidable challenge for Large Language Models (LLMs), particularly in specialized domains where reasoning requires more than surface-level correlations. Retrieval-Augmented Generation (RAG) improves factual accuracy, but standard RAG pipelines treat evidence as flat context, lacking the structure required to model true causal dependencies. We introduce Causal-Chain RAG (CC-RAG), a novel approach that integrates zero-shot triple extraction and theme-aware graph chaining into the RAG pipeline, enabling structured multi-hop inference. Given a domain specific corpus, CC-RAG constructs a Directed Acyclic Graph (DAG) of <cause, relation, effect> triples and uses forward/backward chaining to guide structured answer generation. Experiments on two real-world domains: Bitcoin price fluctuations and Gaucher disease, show that CC-RAG outperforms standard RAG and zero-shot LLMs in chain similarity, information density, and lexical diversity. Both LLM-as-a-Judge and human evaluations consistently favor CC-RAG. Our results demonstrate that explicitly modeling causal structure enables LLMs to generate more accurate and interpretable responses, especially in specialized domains where flat retrieval fails.

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