If your cross-encoder provides better accuracy than your bi-encoder but is too slow for production, you have three practical options: optimize the cross-encoder, combine it with a bi-encoder in a hybrid approach, or improve the bi-encoder to close the accuracy gap. Each approach balances speed and accuracy differently, and the best choice depends on your specific constraints.
First, consider optimizing the cross-encoder itself. Cross-encoders process input pairs jointly, which is computationally expensive. You can reduce latency by using model distillation to train a smaller, faster version of the cross-encoder. For example, a distilled BERT model might retain 90% of the accuracy while running 2-3x faster. Quantization (reducing numerical precision from 32-bit to 16-bit or 8-bit) and hardware optimizations (using ONNX Runtime or TensorRT) can further speed up inference. Additionally, batch processing multiple inputs at once on GPUs can improve throughput. These optimizations don’t eliminate the speed gap entirely but make the cross-encoder more viable for near-real-time use cases.
A hybrid approach combines the bi-encoder’s speed with the cross-encoder’s accuracy. Use the bi-encoder to quickly retrieve a candidate set (e.g., top 100 results), then apply the cross-encoder to rerank only those candidates. For example, in search systems, the bi-encoder filters documents efficiently, while the cross-encoder refines the final ranking. This reduces the cross-encoder’s workload from processing millions of pairs to just hundreds, cutting latency significantly. You can also cache frequent queries or precompute cross-encoder scores for common inputs. This strategy works well when the bi-encoder’s candidate recall is high enough to include most relevant items, letting the cross-encoder focus on precision.
Finally, improve the bi-encoder to reduce the accuracy gap. Bi-encoders are inherently faster because they encode inputs independently, but their performance depends heavily on training data and architecture. Use techniques like contrastive learning with hard negatives (e.g., training with difficult examples the model initially gets wrong) or knowledge distillation from the cross-encoder. For instance, train the bi-encoder to mimic the cross-encoder’s pairwise scores via mean squared error loss. You can also experiment with better pooling strategies (e.g., CLS token vs. mean pooling) or add lightweight cross-attention layers post-encoding. If optimized well, a bi-encoder can approach cross-encoder accuracy while maintaining inference speeds suitable for real-time applications like chatbots or large-scale search.