AI RESEARCH
KCS: Diversify Multi-hop Question Generation with Knowledge Composition Sampling
arXiv CS.CL
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ArXi:2508.20567v2 Announce Type: replace Multi-hop question answering faces substantial challenges due to data sparsity, which increases the likelihood of language models learning spurious patterns. To address this issue, prior research has focused on diversifying question generation through content planning and varied expression. However, these approaches often emphasize generating simple questions and neglect the integration of essential knowledge, such as relevant sentences within documents. This paper