用分层因果图提升RAG推理可信度，解决虚假关联问题。
CausalRAG2: Hierarchical Causal Knowledge Graph Design for RAG
- 构建分层因果图，通过因果门控实现跨模块推理
- 在多个数据集上超越现有基线，准确率显著提升
- 适合需要高可信推理的复杂知识问答场景
检索增强生成（RAG）通过引入外部知识提升了大模型能力，基于图的RAG成为结构化检索与推理的有效范式。然而，现有方法过度依赖实体中心节点匹配，缺乏显式因果建模，导致回答不忠实或存在虚假关联。此前的因果融合多局限于局部或单文档场景，且受模块化图结构导致的信息隔离影响，难以扩展并实现跨模块因果推理。为此，我们提出CausalRAG2，通过分层模块间的因果门控重新设计知识组织方式，显式建模因果关系以抑制虚假相关性，同时支持大规模知识图谱上的可扩展推理。我们还构建了HolisQA基准，用于评估超越实体匹配的全面理解能力。大量实验表明，CausalRAG2在多个数据集和评估指标上持续优于主流图基RAG基线。本工作为结构化、可扩展、因果驱动的RAG系统奠定了原则性基础。代码与HolisQA基准已开源。
原文摘要 · Abstract (English)
Retrieval augmented generation (RAG) has enhanced large language models by enabling access to external knowledge, with graph-based RAG emerging as a powerful paradigm for structured retrieval and reasoning. However, existing graph-based methods often over-rely on entity-centric node matching and lack explicit causal modeling, leading to unfaithful or spurious answers. Prior attempts to incorporate causality are typically limited to local or single-document contexts and also suffer from information isolation that arises from modular graph structures, which hinders scalability and cross-module causal reasoning. To address these challenges, we propose CausalRAG2, a framework that rethinks knowledge organization for graph-based RAG through causal gating across hierarchical modules. CausalRAG2 explicitly models causal relationships to suppress spurious correlations while enabling scalable reasoning over large-scale knowledge graphs. We also introduce HolisQA, a benchmark for holistic comprehension beyond entity-centric matching. Extensive experiments demonstrate that CausalRAG2 consistently outperforms competitive graph-based RAG baselines across multiple datasets and evaluation metrics. Our work establishes a principled foundation for structured, scalable, and causally grounded RAG systems. Our code and HolisQA benchmark are available at https://github.com/Pwnb/CausalRAG2.
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