What is the difference between using a Sentence Transformer (bi-encoder) and a cross-encoder for sentence similarity tasks?

Sentence Transformers (bi-encoders) and cross-encoders are two distinct approaches for sentence similarity tasks, differing in architecture, efficiency, and use cases. A bi-encoder processes each sentence independently, generating fixed-dimensional vector embeddings. These embeddings are then compared (e.g., using cosine similarity) to measure similarity. For example, a bi-encoder might encode “How old are you?” and “What is your age?” into vectors and compute their similarity. In contrast, a cross-encoder processes both sentences together in a single forward pass, using attention mechanisms to analyze interactions between tokens in the pair. This allows it to capture nuanced relationships but requires processing every sentence pair individually, making it slower for large datasets.

Bi-encoders excel in scenarios requiring speed and scalability, such as retrieving similar sentences from a large database. Since embeddings can be precomputed and stored, comparing new queries against millions of entries is efficient. For instance, a search engine might use a bi-encoder to index product descriptions, enabling fast real-time searches. Cross-encoders, however, prioritize accuracy over speed. They are ideal for tasks like reranking top candidates from a bi-encoder’s initial results or evaluating small datasets where precision is critical. For example, after a bi-encoder retrieves 100 potential matches for a legal document, a cross-encoder could reorder them by deeply analyzing context, such as distinguishing between “contract termination by the client” and “client-initiated agreement dissolution.”

The trade-offs between the two hinge on performance needs and resource constraints. Bi-encoders require more upfront training to ensure embeddings generalize well but are cost-effective for production systems. Cross-encoders, while more accurate, are impractical for large-scale tasks due to their O(n²) complexity. A hybrid approach is common: use a bi-encoder for initial candidate retrieval and a cross-encoder for final ranking. Libraries like sentence-transformers (for bi-encoders) and Hugging Face’s transformers (for cross-encoders) simplify implementation. For example, the all-MiniLM-L6-v2 bi-encoder can handle bulk encoding, while a cross-encoder like cross-encoder/ms-marco-TinyBERT-L-6 refines results. Developers must balance latency, accuracy, and computational resources when choosing between these models.