This paper introduces a simple yet effective query expansion approach, denoted as
query2doc, to improve both sparse and dense retrieval systems. The proposed method first …

Recent years have witnessed a substantial increase in the use of deep learning to solve
various natural language processing (NLP) problems. Early deep learning models were …

Current query expansion models use pseudo-relevance feedback to improve first-pass
retrieval effectiveness; however, this fails when the initial results are not relevant. Instead of …

We propose EAR, a query Expansion And Reranking approach for improving passage
retrieval, with the application to open-domain question answering. EAR first applies a query …

Pseudo-relevance feedback (PRF) is a classical approach to address lexical mismatch by
enriching the query using first-pass retrieval. Moreover, recent work on generative-relevance …

Recent advances in large language models (LLMs) have enabled autonomous agents with
complex reasoning and task-fulfillment capabilities using a wide range of tools. However …

Retrieval-enhanced machine learning (REML) refers to the use of information retrieval
methods to support reasoning and inference in machine learning tasks. Although relatively …

Recent studies show that Generative Relevance Feedback (GRF), using text generated by
Large Language Models (LLMs), can enhance the effectiveness of query expansion …

In retrieval-augmented systems, context ranking techniques are commonly employed to
reorder the retrieved contexts based on their relevance to a user query. A standard …

Addressing the" vocabulary mismatch" issue in information retrieval is a central challenge for
e-commerce search engines, because product pages often miss important keywords that …