Model that scores a query and a passage together in one forward pass.
No comparable attributes have been recorded for these two technologies yet.
Generated from the differences above by structured rules, not by a model.
Choose Cross-Encoder if…
Nothing in the recorded attributes distinguishes it in your favour.
Choose Bi-Encoder if…
Nothing in the recorded attributes distinguishes it in your favour.
The page is kept reachable but excluded from search indexes until it carries enough sourced detail (PRD SEO-08).
concept · Techniques
A cross-encoder concatenates the query and one candidate passage and runs them through the model together, producing a relevance score for that pair. It solves the problem that independent embeddings cannot represent how a specific query relates to a specific passage. It exists as the accurate but expensive half of retrieval: accurate enough to reorder a shortlist, too slow to search a corpus with.
concept · Techniques
A bi-encoder encodes the query and each passage independently into vectors, and relevance is their similarity. It solves the problem that a cross-encoder must run once per candidate, which is impossible against a large collection. It exists as the half of retrieval that can be precomputed and indexed, at the cost of losing the interaction between query and passage.
