A Sentence Transformer (bi-encoder) and a cross-encoder differ primarily in how they process text pairs for similarity tasks. A bi-encoder encodes two sentences independently into fixed-dimensional embeddings, then computes similarity (e.g., cosine similarity) between them. This approach is efficient because embeddings for a large corpus can be precomputed and reused, making it scalable for tasks like retrieval where speed is critical. For example, in a search system, a bi-encoder can quickly compare a query against millions of pre-encoded documents. However, because the sentences are processed separately, bi-encoders may miss nuanced interactions between tokens in the pair.
In contrast, a cross-encoder processes both sentences together in a single forward pass. This allows the model to use attention mechanisms to analyze relationships between tokens across the two sentences directly. Cross-encoders typically achieve higher accuracy in tasks like semantic textual similarity (STS) because they capture fine-grained interactions. For instance, when evaluating whether "The cat sat on the mat" and "A feline rested on the rug" are similar, a cross-encoder can recognize synonym relationships (cat/feline, mat/rug) through token-level attention. However, this comes at a computational cost: comparing a query to a large dataset requires processing every pair individually, which is impractical for real-time applications.
The choice between the two depends on the use case. Bi-encoders are ideal for scenarios requiring low latency and scalability, such as candidate retrieval in search systems. Cross-encoders are better suited for tasks where accuracy is paramount and computational resources are available, such as reranking top candidates from a bi-encoder’s initial results. A common hybrid approach is to use a bi-encoder for fast retrieval (e.g., fetching 100 candidates) and a cross-encoder to refine the ranking of those candidates, balancing speed and precision. For example, a question-answering system might first retrieve relevant passages with a bi-encoder, then use a cross-encoder to identify the best match.