What Tracks Hard-Negative Transfer? A Study of Lexical and Semantic Signals
arXiv:2603.20990v5 Announce Type: replace-cross Abstract: Which properties of hard-negative supervision track dense-retriever transfer before training? We compare lexical noncoverage, frozenencoder similarity statistics, hardness proxies, and combinations of these signals across BM25, dense, LLM-generated, and hybrid sources for MS MARCO training. Evaluation uses three retriever backbones and eleven BEIR datasets. Mean lexical residuality, an IDF-weighted measure of query-token noncoverage, rep…