If you need faster predictions than a cross-encoder can provide while retaining some of its accuracy advantages over a bi-encoder, consider these three approaches:
1. Use Knowledge Distillation to Improve the Bi-encoder Train your bi-encoder to mimic the cross-encoder’s behavior. For example, generate pairwise similarity scores (e.g., for query-document pairs) using the cross-encoder, then fine-tune the bi-encoder to reproduce those scores. This transfers the cross-encoder’s nuanced understanding into the bi-encoder’s faster architecture. For instance, if your cross-encoder uses BERT, you could train a smaller bi-encoder like Sentence-BERT (SBERT) using the cross-encoder’s outputs as labels. While this won’t fully match cross-encoder accuracy, it bridges the gap without sacrificing bi-encoder speed.
2. Deploy a Hybrid Retrieval-Reranking Pipeline Use the bi-encoder for initial fast retrieval and the cross-encoder for limited reranking. For example, retrieve 100 candidates with the bi-encoder, then rerank the top 10 using the cross-encoder. This reduces the cross-encoder’s workload to a fraction of the original dataset. In search systems, this is a common pattern: the bi-encoder handles scalability (e.g., using approximate nearest neighbor libraries like FAISS), while the cross-encoder refines the final results. Adjust the candidate pool size (e.g., top 20 vs. top 50) to balance latency and accuracy.
3. Optimize the Cross-encoder for Speed Reduce the cross-encoder’s inference time without drastically cutting accuracy. Techniques include:
- Model Pruning: Remove less important neurons or layers.
- Quantization: Use 8-bit integers instead of 32-bit floats for weights.
- Architecture Choice: Switch to a smaller pretrained model (e.g., TinyBERT instead of BERT-base). For example, a distilled cross-encoder like MiniLM (a compact version of RoBERTa) can retain ~95% of the accuracy while running 2–3× faster. Tools like ONNX Runtime or TensorRT can further optimize inference speed.
By combining these strategies, you can tailor the trade-off between accuracy and latency to your specific needs. For instance, a distilled bi-encoder might suffice for moderate accuracy gains, while a hybrid approach works for critical tasks needing near-cross-encoder performance.