Neural information retrieval often adopts a retrieve-and-rerank framework: a
bi-encoder network first retrieves K (e.g., 100) candidates that are then
re-ranked using a more powerful cross-encoder model to rank the better
candidates higher. The re-ranker generally produces better candidate scores
than the retriever, but is limited to seeing only the top K retrieved
candidates, thus providing no improvements in retrieval performance as measured
by Recall@K. In this work, we leverage the re-ranker to also improve retrieval
by providing inference-time relevance feedback to the retriever. Concretely, we
update the retriever's query representation for a test instance using a
lightweight inference-time distillation of the re-ranker's prediction for that
instance. The distillation loss is designed to bring the retriever's candidate
scores closer to those of the re-ranker. A second retrieval step is then
performed with the updated query vector. We empirically show that our approach,
which can serve arbitrary retrieve-and-rerank pipelines, significantly improves
retrieval recall in multiple domains, languages, and modalities.

本文提出了利用轻量级的推理，将 re-ranker 的命中率反馈给 retriever 的方法，从而提高检索召回率。实验证明，此方法在多个领域、语言和模态下均明显提高了检索召回率。