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arXiv AI By Aarush Sinha, Rahul Seetharaman, Aman Bansal

What Tracks Hard-Negative Transfer? A Study of Lexical and Semantic Signals

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The study investigates which properties of hard‑negative supervision predict dense‑retriever transfer before training. It compares lexical non‑coverage, frozen‑encoder similarity, hardness proxies, and their combinations across BM25, dense, LLM‑generated, and hybrid sources for MS MARCO training. The results show that mean lexical residuality, an IDF‑weighted measure of query‑token non‑coverage, consistently reproduces the aggregate transfer ordering, while semantic statistics alone or combined with lexical signals do not improve rank agreement.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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