How does a cross-encoder operate differently from a bi-encoder, and when might you use one over the other?

Cross-encoders and bi-encoders are two architectures used for tasks involving pairs of text, like similarity scoring or question answering. The key difference lies in how they process input pairs. A bi-encoder processes each input (e.g., a query and a document) separately through a shared neural network, generating independent embeddings. These embeddings are then compared using a similarity metric like cosine similarity. For example, in a search system, a bi-encoder might encode all documents into vectors upfront and compare them to a query vector at runtime. In contrast, a cross-encoder processes both inputs together in a single forward pass, allowing the model to directly analyze interactions between tokens in the pair. This joint processing enables deeper contextual understanding but requires more computation.

Bi-encoders are typically used in scenarios where speed and scalability are critical. Since embeddings can be precomputed for items like documents or product descriptions, comparing a new query to millions of pre-stored vectors is efficient. For instance, a recommendation system might use a bi-encoder to match user queries to products by pre-encoding product data once and reusing it for fast lookups. Cross-encoders, however, excel when accuracy is the priority and computational cost is manageable. Tasks like reranking top search results benefit from cross-encoders because they can analyze subtle contextual relationships between a query and a small subset of candidates. For example, after a bi-encoder retrieves 100 potential answers, a cross-encoder could reorder them by more precisely judging relevance.

The choice between architectures depends on trade-offs. Bi-encoders are optimal for real-time applications with large datasets, as their two-step encoding-comparison process scales well. Cross-encoders, while slower, provide higher accuracy for fine-grained tasks like semantic textual similarity or natural language inference (e.g., determining if one sentence contradicts another). A hybrid approach is common: use a bi-encoder for initial candidate retrieval and a cross-encoder for final ranking. For developers, the decision hinges on balancing latency, resource constraints, and performance needs. If your system requires instant responses (e.g., autocomplete suggestions), a bi-encoder is practical. If precision is non-negotiable (e.g., legal document analysis), a cross-encoder may justify the computational overhead.