The overhead of using a cross-encoder for reranking stems from its computational complexity. A bi-encoder processes queries and documents independently, generating fixed-dimensional embeddings. Comparing these embeddings (e.g., via cosine similarity) is fast, especially with vector databases or approximate nearest neighbor search. In contrast, a cross-encoder jointly processes each query-document pair, enabling deeper interaction but requiring full inference for every candidate pair. For example, reranking 1,000 documents with a bi-encoder involves one query embedding and 1,000 document embeddings, while a cross-encoder requires 1,000 separate inference steps. This makes cross-encoders orders of magnitude slower and more resource-intensive, particularly with large candidate sets or long input sequences.
To minimize this cost, reduce the number of documents processed by the cross-encoder. Use the bi-encoder to retrieve a smaller subset (e.g., top 100 candidates) and apply the cross-encoder only to these. This hybrid approach balances speed and accuracy, as the bi-encoder efficiently narrows the pool. Optimize the cross-encoder itself by using distillation (training a smaller model to mimic the original) or quantization (reducing numerical precision for faster inference). For instance, a 6-layer distilled BERT model might retain 90% of the accuracy while running 2x faster than a 12-layer BERT. Additionally, leverage hardware optimizations like GPU/TPU acceleration and batch processing—grouping multiple query-document pairs into a single batch to exploit parallel computation.
Implement caching for frequent queries and documents to avoid redundant cross-encoder runs. For example, in a FAQ system, precompute and cache cross-encoder scores for common questions paired with top answers. Use dynamic batching frameworks (e.g., NVIDIA Triton) to handle variable-length inputs efficiently. If latency is critical, consider model pruning to remove less important neurons or layers, reducing inference time. Finally, evaluate trade-offs: a bi-encoder with a cross-encoder reranking step often provides better accuracy than either model alone, but the exact balance depends on use-case requirements (e.g., 50ms latency might necessitate limiting cross-encoder to 20 candidates, while batch systems prioritize throughput).