用因果与反事实推理增强RAG，让AI回答更可信
Causal-Counterfactual RAG: The Integration of Causal-Counterfactual Reasoning into RAG
- 将因果图融入检索，分析事件间的因果关系
- 结合假设性反事实推演，提升答案准确性和可解释性
- 适合需要严谨推理的医疗、法律等专业领域
大语言模型在自然语言处理中取得突破，但其静态知识限制了对外部信息的动态推理，尤其在知识密集型领域。检索增强生成（RAG）通过融合检索与生成来改善上下文理解，但传统方法因文本分块和过度依赖语义相似度，常导致上下文断裂与浅层回答。本文提出因果-反事实RAG，将显式因果图嵌入检索过程，并基于因果结构引入反事实推理。该框架不仅评估直接因果证据，还分析相关原因的反事实可能性，综合两者生成更鲁棒、准确且可解释的答案。通过利用因果路径与假设情景，该方法保持上下文连贯性，减少幻觉，提升推理真实性。
原文摘要 · Abstract (English)
Large language models (LLMs) have transformed natural language processing (NLP), enabling diverse applications by integrating large-scale pre-trained knowledge. However, their static knowledge limits dynamic reasoning over external information, especially in knowledge-intensive domains. Retrieval-Augmented Generation (RAG) addresses this challenge by combining retrieval mechanisms with generative modeling to improve contextual understanding. Traditional RAG systems suffer from disrupted contextual integrity due to text chunking and over-reliance on semantic similarity for retrieval, often resulting in shallow and less accurate responses. We propose Causal-Counterfactual RAG, a novel framework that integrates explicit causal graphs representing cause-effect relationships into the retrieval process and incorporates counterfactual reasoning grounded on the causal structure. Unlike conventional methods, our framework evaluates not only direct causal evidence but also the counterfactuality of associated causes, combining results from both to generate more robust, accurate, and interpretable answers. By leveraging causal pathways and associated hypothetical scenarios, Causal-Counterfactual RAG preserves contextual coherence, reduces hallucination, and enhances reasoning fidelity.
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