Bi-encoders and cross-encoders are two architectures used in natural language processing (NLP) for tasks like text similarity, retrieval, or ranking. The key difference lies in how they process input pairs. Bi-encoders encode two text inputs separately into fixed-dimensional vectors and then compute similarity (e.g., using cosine similarity). Cross-encoders, on the other hand, process both inputs together in a single pass, allowing direct interaction between tokens of both texts. This distinction impacts their performance, speed, and suitable use cases.
Bi-encoders are efficient for large-scale similarity tasks. For example, in a search engine, you might encode millions of documents into vectors offline. When a user query comes in, you encode it and find the closest matches via vector similarity (e.g., using approximate nearest neighbor libraries like FAISS). Because embeddings are precomputed, bi-encoders scale well. However, since the two texts are encoded independently, they may miss nuanced interactions. A common implementation involves using a model like BERT to encode each input separately. Cross-encoders, meanwhile, concatenate both inputs (e.g., a query and a document) and pass them through the model simultaneously. This allows the model to weigh relationships between specific words in both texts, leading to higher accuracy for tasks like reranking top candidates. For instance, after a bi-encoder retrieves 100 potential matches, a cross-encoder could reorder them by relevance. The downside is computational cost: processing every input pair is impractical for large datasets.
Use bi-encoders when speed and scalability are critical, such as in real-time search or recommendation systems where you need to process thousands of candidates quickly. Cross-encoders are better suited for smaller-scale tasks requiring high precision, like final ranking or pairwise classification (e.g., duplicate question detection). For example, a customer support chatbot might use a bi-encoder to quickly find relevant FAQ entries, then apply a cross-encoder to pick the single best answer. Hybrid approaches are common: use a bi-encoder for initial candidate retrieval and a cross-encoder for refinement. The choice ultimately depends on balancing latency, resource constraints, and accuracy needs for your specific application.