用因果图提升检索生成，让回答更准确可解释
CausalRAG: Integrating Causal Graphs into Retrieval-Augmented Generation
- 用因果图替代单纯语义匹配，增强知识关联性
- 在多个任务上优于传统RAG和基于图的RAG
- 适合需要逻辑严谨性的问答与推理场景
大型语言模型（LLM）通过检索增强生成（RAG）技术显著提升了自然语言处理能力，但传统RAG存在因文本分块导致上下文断裂、过度依赖语义相似度等问题。为此，我们提出CausalRAG框架，将因果图融入检索过程，通过构建并追踪因果关系，保持上下文连续性并提升检索精度，从而生成更准确、可解释的回答。我们在多个基准上对比常规RAG与基于图的RAG，结果表明CausalRAG在各项指标上均表现更优。研究显示，以因果推理为基础的检索机制为知识密集型任务提供了有效解决方案。
原文摘要 · Abstract (English)
Large language models (LLMs) have revolutionized natural language processing (NLP), particularly through Retrieval-Augmented Generation (RAG), which enhances LLM capabilities by integrating external knowledge. However, traditional RAG systems face critical limitations, including disrupted contextual integrity due to text chunking, and over-reliance on semantic similarity for retrieval. To address these issues, we propose CausalRAG, a novel framework that incorporates causal graphs into the retrieval process. By constructing and tracing causal relationships, CausalRAG preserves contextual continuity and improves retrieval precision, leading to more accurate and interpretable responses. We evaluate CausalRAG against regular RAG and graph-based RAG approaches, demonstrating its superiority across several metrics. Our findings suggest that grounding retrieval in causal reasoning provides a promising approach to knowledge-intensive tasks.
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