Do Neural Retrievers Prefer Certain Documents? Evidence of Learned Relevance Priors
Neural retrievers are trained to estimate query-document relevance from annotated query-document pairs. Yet annotation protocols may not purely reflect relevance: they select only a subset of documents for labeling, and this selection can favor certain document types over others. We investigate whether supervised bi-encoder retrievers implicitly learn a document-level relevance prior: a query-independent signal encoded in their representation space as a side effect of training on annotated data. We estimate this prior by training simple classifiers on frozen document embeddings and evaluate three state-of-the-art retrievers across multiple IR benchmarks. We find that supervised neural ret...
| Authors | Francisco Valentini, Edgar Altszyler, Martin Fajcik |
|---|---|
| Primary category | cs.IR |
| Published | 1 June 2026 |
| Last updated at source | 1 June 2026 |
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