用检索到的证据动态优化查询，提升多跳图检索准确率
EviReform: Evidence-Guided Query Reformulation for Multi-Hop Graph Retrieval
- 根据已检索文本生成残差查询，动态修正后续检索目标
- 在2WikiMultiHopQA等数据集上，召回率最高提升5.59点
- 适合需要精准推理链的问答系统开发者
多跳检索需整合多个段落以提供充分证据。初始段落常揭示问题中隐含的实体或关系，使未满足的信息需求更易描述。图检索通过存储语料结构提升关联证据访问效率，但其检索信号通常来自原始问题。当已有段落提供更直接语义线索时，仍需依赖存储关系寻找互补证据。我们提出EviReform，将查询改写与图中证据聚合分离处理。已检索源段落生成针对未解决信息需求的残差查询，原始与残差检索信号分别归一化后融合，并在共享实体的命题间传播。在2WikiMultiHopQA、HotpotQA和MuSiQue数据集上，EviReform相比最强基线最高提升5.59 Recall@5点和4.50 F1点。结果表明，观测到的证据可引导图检索聚焦于原问题未明确定义的支持链部分。代码已开源。
原文摘要 · Abstract (English)
Multi-hop retrieval must recover passages that provide sufficient evidence together. An initial passage often resolves an entity or relation implicit in the question, making the missing evidence easier to describe only after retrieval begins. Graph retrieval improves access to related evidence through stored corpus structure, but its retrieval signal is commonly derived from the original question. Complementary evidence must then be reached through stored relations even when an observed passage provides a more direct semantic cue. We introduce EviReform, which separates revising the retrieval request from aggregating evidence in the graph. Retrieved source passages formulate residual queries for the unresolved information need. The original and residual retrieval signals are normalized separately, combined, and propagated between propositions that share entities. On 2WikiMultiHopQA, HotpotQA, and MuSiQue, EviReform exceeds the strongest baseline by up to 5.59 Recall@5 points and 4.50 F1 points. These results show that observed evidence can guide graph retrieval toward the part of a supporting chain left underspecified by the original question. Code is available at https://github.com/XrazyMee/EviReform.
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