作者位次 #2/6ACL 2026Poster· Natural Language Processing
H op W eaver: Cross-Document Synthesis of High-Quality and Authentic Multi-Hop Questions
Zhiyu Shen · Jiyuan Liu · Yunhe Pang · Yanghui Rao · Fu Lee Wang · Jianxing Yu
摘要
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 simpl...