ReFIT: Relevance Feedback from a Reranker during Inference

• URL: http://arxiv.org/abs/2305.11744v2
• Date: Tue, 28 May 2024 17:12:02 GMT
• Title: ReFIT: Relevance Feedback from a Reranker during Inference
• Authors: Revanth Gangi Reddy, Pradeep Dasigi, Md Arafat Sultan, Arman Cohan, Avirup Sil, Heng Ji, Hannaneh Hajishirzi,
• Abstract summary: Retrieve-and-rerank is a prevalent framework in neural information retrieval. We propose to leverage the reranker to improve recall by making it provide relevance feedback to the retriever at inference time.
• Score: 109.33278799999582
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Towards Competitive Search Relevance For Inference-Free Learned Sparse Retrievers [6.773411876899064]
  inference-free sparse models lag far behind in terms of search relevance when compared to both sparse and dense siamese models. We propose two different approaches for performance improvement. First, we introduce the IDF-aware FLOPS loss, which introduces Inverted Document Frequency (IDF) to the sparsification of representations. We find that it mitigates the negative impact of the FLOPS regularization on search relevance, allowing the model to achieve a better balance between accuracy and efficiency.
  arXiv  Detail & Related papers  (2024-11-07T03:46:43Z)
• Improve Dense Passage Retrieval with Entailment Tuning [22.39221206192245]
  Key to a retrieval system is to calculate relevance scores to query and passage pairs. We observed that a major class of relevance aligns with the concept of entailment in NLI tasks. We design a method called entailment tuning to improve the embedding of dense retrievers.
  arXiv  Detail & Related papers  (2024-10-21T09:18:30Z)
• FIRST: Faster Improved Listwise Reranking with Single Token Decoding [56.727761901751194]
  First, we introduce FIRST, a novel listwise LLM reranking approach leveraging the output logits of the first generated identifier to directly obtain a ranked ordering of the candidates. Empirical results demonstrate that FIRST accelerates inference by 50% while maintaining a robust ranking performance with gains across the BEIR benchmark. Our results show that LLM rerankers can provide a stronger distillation signal compared to cross-encoders, yielding substantial improvements in retriever recall after relevance feedback.
  arXiv  Detail & Related papers  (2024-06-21T21:27:50Z)
• Ask Optimal Questions: Aligning Large Language Models with Retriever's Preference in Conversational Search [25.16282868262589]
  RetPO is designed to optimize a language model (LM) for reformulating search queries in line with the preferences of the target retrieval systems. We construct a large-scale dataset called Retrievers' Feedback on over 410K query rewrites across 12K conversations. The resulting model achieves state-of-the-art performance on two recent conversational search benchmarks.
  arXiv  Detail & Related papers  (2024-02-19T04:41:31Z)
• Improving Out-of-Distribution Generalization of Neural Rerankers with Contextualized Late Interaction [52.63663547523033]
  Late interaction, the simplest form of multi-vector, is also helpful to neural rerankers that only use the [] vector to compute the similarity score. We show that the finding is consistent across different model sizes and first-stage retrievers of diverse natures.
  arXiv  Detail & Related papers  (2023-02-13T18:42:17Z)
• Bridging the Training-Inference Gap for Dense Phrase Retrieval [104.4836127502683]
  Building dense retrievers requires a series of standard procedures, including training and validating neural models. In this paper, we explore how the gap between training and inference in dense retrieval can be reduced. We propose an efficient way of validating dense retrievers using a small subset of the entire corpus.
  arXiv  Detail & Related papers  (2022-10-25T00:53:06Z)
• ReAct: Temporal Action Detection with Relational Queries [84.76646044604055]
  This work aims at advancing temporal action detection (TAD) using an encoder-decoder framework with action queries. We first propose a relational attention mechanism in the decoder, which guides the attention among queries based on their relations. Lastly, we propose to predict the localization quality of each action query at inference in order to distinguish high-quality queries.
  arXiv  Detail & Related papers  (2022-07-14T17:46:37Z)
• Optimizing Test-Time Query Representations for Dense Retrieval [34.61821330771046]
  TOUR improves query representations guided by test-time retrieval results. We leverage a cross-encoder re-ranker to provide fine-grained pseudo labels over retrieval results. TOUR consistently improves direct re-ranking by up to 2.0% while running 1.3-2.4x faster.
  arXiv  Detail & Related papers  (2022-05-25T11:39:42Z)
• Adversarial Retriever-Ranker for dense text retrieval [51.87158529880056]
  We present Adversarial Retriever-Ranker (AR2), which consists of a dual-encoder retriever plus a cross-encoder ranker. AR2 consistently and significantly outperforms existing dense retriever methods. This includes the improvements on Natural Questions R@5 to 77.9%(+2.1%), TriviaQA R@5 to 78.2%(+1.4), and MS-MARCO MRR@10 to 39.5%(+1.3%)
  arXiv  Detail & Related papers  (2021-10-07T16:41:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.