构建时间因果一致性评测集，提出双图框架提升叙事文档问答准确率
Respecting Temporal-Causal Consistency: Entity-Event Knowledge Graphs for Retrieval-Augmented Generation
- 分离实体与事件建图，通过双向映射保持时间因果关系
- 在ChronoQA上超越现有基线，因果与人物一致性任务提升显著
- 适合需要精确时序推理的长文本问答场景
基于大语言模型的检索增强生成（RAG）在具有内在时间结构的叙事文档中表现不佳。标准无结构RAG仅依赖嵌入相似性匹配，缺乏编码和利用时间信息的通用机制；而知识图谱RAG框架将所有实体提及合并为单一节点，抹去了驱动许多问题的动态上下文。为此，我们构建了ChronoQA——一个稳健且具有区分度的问答基准，用于评估在RAG设置下对叙事文档（如小说）中的时间、因果及角色一致性理解能力。随后提出实体-事件RAG（E^2RAG），一种双图框架，分别维护实体子图与事件子图，并通过二部图映射关联，从而保留细粒度推理所需的时间与因果特征。在ChronoQA上，该方法优于当前最优的无结构与基于知识图谱的RAG基线，尤其在因果与角色一致性查询上表现突出。E^2RAG为需要精确时序上下文的任务提供了更具情境感知的实用检索路径。
原文摘要 · Abstract (English)
Retrieval-augmented generation (RAG) based on large language models often falters on narrative documents with inherent temporal structures. Standard unstructured RAG methods rely solely on embedding-similarity matching and lack any general mechanism to encode or exploit chronological information, while knowledge graph RAG (KG-RAG) frameworks collapse every mention of an entity into a single node, erasing the evolving context that drives many queries. To formalize this challenge and draw the community's attention, we construct ChronoQA, a robust and discriminative QA benchmark that measures temporal, causal, and character consistency understanding in narrative documents (e.g., novels) under the RAG setting. We then introduce Entity-Event RAG (E^2RAG), a dual-graph framework that keeps separate entity and event subgraphs linked by a bipartite mapping, thereby preserving the temporal and causal facets needed for fine-grained reasoning. Across ChronoQA, our approach outperforms state-of-the-art unstructured and KG-based RAG baselines, with notable gains on causal and character consistency queries. E^2RAG therefore offers a practical path to more context-aware retrieval for tasks that require precise answers grounded in chronological information.
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