Bi-encoders and cross-encoders are two architectures used in natural language processing (NLP) for tasks like semantic similarity, retrieval, or ranking. The key difference lies in how they process input pairs. Bi-encoders encode two inputs (e.g., a query and a document) separately into fixed-dimensional vectors, then compare their similarity using metrics like cosine similarity. Cross-encoders, on the other hand, process both inputs together in a single forward pass, allowing direct interaction between the two texts through attention mechanisms. This fundamental distinction leads to trade-offs in efficiency, accuracy, and use cases.
Bi-encoders are optimized for speed and scalability. For example, in a search engine, you might encode millions of documents into vectors offline using a model like BERT. When a user submits a query, it’s encoded into the same vector space, and a nearest-neighbor search retrieves the closest matches. Since the encodings are precomputed, this approach is fast even for large datasets. However, because the two texts are processed independently, bi-encoders may miss nuanced relationships that require direct interaction between the inputs. A common implementation involves "dual towers" in neural networks, where separate but identical encoders process each input. Developers often use bi-encoders for tasks like candidate retrieval, where speed outweighs the need for perfect precision.
Cross-encoders sacrifice efficiency for accuracy. By concatenating both inputs (e.g., a query and a candidate document) and processing them jointly, the model can analyze how specific words or phrases in one text relate to the other. For instance, in a question-answering system, a cross-encoder could deeply evaluate whether a sentence contains the answer by examining subtle contextual cues. This makes cross-encoders ideal for reranking a small set of candidates (e.g., the top 100 results from a bi-encoder) where precision is critical. However, because they process pairs in real-time, cross-encoders are impractical for large-scale tasks. A typical use case is fine-tuning models like RoBERTa to classify pairs (e.g., "Is this sentence a valid answer to the query?") by outputting a relevance score directly rather than a vector. Developers often combine both approaches: bi-encoders for fast candidate retrieval and cross-encoders for final ranking.