ReFIT: Relevance Feedback from a Reranker during Inference

Understand

Retrieve-and-rerank is a prevalent framework in neural information retrieval, wherein a bi-encoder network initially retrieves a pre-defined number of candidates (e.g., K=100), which are then reranked by a more powerful cross-encoder model.

• While the reranker often yields improved candidate scores compared to the retriever, its scope is confined to only the top K retrieved candidates.
• As a result, the reranker cannot improve retrieval performance in terms of Recall@K.
• In this work, we propose to leverage the reranker to improve recall by making it provide relevance feedback to the retriever at inference time.