ReFIT：推理期间来自重排序器的相关性反馈
ReFIT: Relevance Feedback from a Reranker during Inference
AI总结：
提出ReFIT方法，在推理时利用重排序器的预测通过轻量级更新机制蒸馏到检索器查询表示中，执行第二次检索以提升Recall@K，实验证明该方法在多个领域、语言和模态中显著提高检索召回率。
摘要：
检索-重排序是神经信息检索中的一种普遍框架，其中双编码器网络首先检索预定数量的候选（例如，K=100），然后由更强大的交叉编码器模型对这些候选进行重排序。虽然重排序器通常比检索器产生改进的候选分数，但其范围仅限于前K个检索到的候选。因此，重排序器无法在Recall@K方面提高检索性能。在这项工作中，我们提出利用重排序器通过使其在推理时向检索器提供相关性反馈来提高召回率。具体来说，给定推理期间的测试实例，我们使用轻量级更新机制将重排序器对该实例的预测蒸馏到检索器的查询表示中。蒸馏损失的目标是使检索器的候选分数与重排序器产生的分数更紧密地对齐。然后，该算法通过使用更新后的查询向量执行第二次检索步骤继续进行。我们通过实验证明，这种方法适用于各种检索-重排序框架，在多个领域、语言和模态中显著提高了检索召回率。
英文摘要：
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. Specifically, given a test instance during inference, we distill the reranker's predictions for that instance into the retriever's query representation using a lightweight update mechanism. The aim of the distillation loss is to align the retriever's candidate scores more closely with those produced by the reranker. The algorithm then proceeds by executing a second retrieval step using the updated query vector. We empirically demonstrate that this method, applicable to various retrieve-and-rerank frameworks, substantially enhances retrieval recall across multiple domains, languages, and modalities.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
