Cross-Encoder Scoring

There are two ways to build scoring systems for retrieval. Dual-encoder: encode your query, encode your document, compare embeddings. Cross-encoder: throw the query AND the document into a transformer together, ask it to score the pair directly. Counterintuitive? Yes. More effective? Often, yes. The difference is architectural. Dual encoders work in isolation. They never see the query-document pair together, so they can't capture interaction patterns specific to that pair. Cross-encoders see the full context and can learn fine-grained relevance signals. A query like 'lightweight Python web frameworks' might encode similarly to 'heavy Python web frameworks' in a dual-encoder world (both are about Python frameworks). But a cross-encoder can distinguish them. It's more computationally expensive though. You can pre-compute dual-encoder embeddings once and reuse them. Cross-encoders must score each query-document pair on-the-fly. So in practice, hybrid approaches are common. Use dual-encoders (cheap, fast) for initial retrieval. Use cross-encoders (expensive, accurate) for reranking the top candidates. This is called retrieval then reranking, and it works really well. Modern cross-encoders are typically BERT-style transformer models fine-tuned on relevance datasets. They learn subtle patterns about what makes documents relevant to specific queries. They're particularly good at understanding negation, specificity, and semantic nuance. The tradeoff: latency versus accuracy. With a million documents, reranking the top 100 candidates with cross-encoders is feasible. Reranking all million is not. Synap's cross-encoder implementation handles both initial scoring and reranking workflows, optimizing for your specific performance requirements and accuracy targets.