AI RESEARCH
HopWeaver: Cross-Document Synthesis of High-Quality and Authentic Multi-Hop Questions
arXiv CS.CL
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ArXi:2505.15087v3 Announce Type: replace Multi-Hop Question Answering (MHQA) is crucial for evaluating the model's capability to integrate information from diverse sources. However, creating extensive and high-quality MHQA datasets is challenging: (i) manual annotation is expensive, and (ii) current synthesis methods often produce simplistic questions or require extensive manual guidance. This paper