Scaling Multi-Hop Training Data via Graph-Constrained Path Selection 事件

Scaling Multi-Hop Training Data via Graph-Constrained Path Selection arXiv:2605.31238v1 Announce Type: new Abstract: Endowing large language models with compositional reasoning over specialized documents requires multi-hop training data at scale, where such data rarely exists outside of curated benchmarks built on structured sources. To construct it directly from plain, unannotated text, existing methods ask a single teacher model to jointly discover an evidence path through a document and verb