HyperProve：面向多跳问答的答案引导超图扩展
HyperProve: Answer-Guided Hypergraph Expansion for Multi-Hop Question Answering
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中文总结 AI 辅助
HyperProve通过将问题分解与答案引导的超图扩展相结合，实现状态化、以事实为中心的检索，在多跳问答基准上显著提升答案准确率和F1。
中文摘要 AI 辅助
多跳问答常常在检索将证据视为与原始问题的孤立匹配时失败，因为回答复杂问题所需的事实通常通过中间实体、关系和约束相互连接。我们提出HyperProve，一种检索增强的问答框架，通过将问题分解与基于答案条件的原子事实超图扩展相结合来解决这一挑战。HyperProve并非孤立地使用原子事实、超图或迭代检索；相反，它将中间答案和支持超边作为检索状态携带，然后利用该状态来偏置下一次局部超图扩展。这种设计使HyperProve能够为最终答案生成构建连贯的证据链，同时使检索过程具有状态性和以事实为中心。在多跳问答基准测试中，HyperProve在我们的评估中取得了最佳整体性能，在答案准确率上平均相对提升6.2%，在F1分数上平均相对提升4.9%，优于最强基线。
英文摘要
Multi-hop question answering often fails when retrieval treats evidence as isolated matches to the original question, since the facts needed to answer a complex question are usually connected through intermediate entities, relations, and constraints. We propose HyperProve, a retrieval-augmented QA framework that addresses this challenge by coupling question decomposition with answer-conditioned expansion over a hypergraph of atomic facts. HyperProve does not use atomic facts, hypergraphs, or iterative retrieval in isolation; instead, it carries intermediate answers and supporting hyperedges as retrieval state, then uses that state to bias the next local hypergraph expansion. This design enables HyperProve to construct coherent evidence chains for final answer generation while making the retrieval process stateful and fact-centered. Across multi-hop QA benchmarks, HyperProve achieves the best overall performance in our evaluation, outperforming the strongest baselines by an average relative improvement of 6.2% in answer accuracy and 4.9% in F1.
发表机构
- Hanoi University of Science and Technology（河内科技大学）
- Monash University（莫纳什大学）
- University of Oregon（俄勒冈大学）
机构由 AI 辅助整理，请以论文原文为准。
