In Defense of Cross-Encoders for Zero-Shot Retrieval
This work addresses the problem of improving retrieval performance in unseen domains for researchers and practitioners in information retrieval, though it is incremental as it builds on existing architectures.
The study investigates the generalization ability of cross-encoders versus bi-encoders in zero-shot retrieval, finding that cross-encoders outperform bi-encoders by over 4 average points on the BEIR benchmark and show larger gains in out-of-domain scenarios.
Bi-encoders and cross-encoders are widely used in many state-of-the-art retrieval pipelines. In this work we study the generalization ability of these two types of architectures on a wide range of parameter count on both in-domain and out-of-domain scenarios. We find that the number of parameters and early query-document interactions of cross-encoders play a significant role in the generalization ability of retrieval models. Our experiments show that increasing model size results in marginal gains on in-domain test sets, but much larger gains in new domains never seen during fine-tuning. Furthermore, we show that cross-encoders largely outperform bi-encoders of similar size in several tasks. In the BEIR benchmark, our largest cross-encoder surpasses a state-of-the-art bi-encoder by more than 4 average points. Finally, we show that using bi-encoders as first-stage retrievers provides no gains in comparison to a simpler retriever such as BM25 on out-of-domain tasks. The code is available at https://github.com/guilhermemr04/scaling-zero-shot-retrieval.git