HyperProve: Answer-Guided Hypergraph Expansion for Multi-Hop Question Answering

2026-09-12

AI Summary

为解决多跳问答中证据孤立匹配问题，HyperProve通过结合问题分解与基于答案的超图扩展方法，实现连贯证据链构建，提高回答准确性。

Abstract

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.

Problem

• multi-hop question answering
• evidence retrieval
• intermediate entities

Innovation

• Answer-Guided Hypergraph Expansion
• Retrieval-Augmented QA Framework
• Intermediate Answers as State
• Supporting Hyperedges