Information retrieval (IR) plays a crucial role in locating relevant
resources from vast amounts of data, and its applications have evolved from
traditional knowledge bases to modern search engines (SEs). The emergence of
large language models (LLMs) has further revolutionized the field by enabling
users to interact with search systems in natural language. In this paper, we
explore the advantages and disadvantages of LLMs and SEs, highlighting their
respective strengths in understanding user-issued queries and retrieving
up-to-date information. To leverage the benefits of both paradigms while
circumventing their limitations, we propose InteR, a novel framework that
facilitates knowledge refinement through interaction between SEs and LLMs.
InteR allows SEs to refine knowledge in query using LLM-generated summaries and
enables LLMs to enhance prompts using SE-retrieved documents. This iterative
refinement process augments the inputs of SEs and LLMs, leading to more
accurate retrieval. Experimental evaluations on two large-scale retrieval
benchmarks demonstrate that InteR achieves superior zero-shot document
retrieval performance compared to state-of-the-art methods, regardless of the
use of relevance judgement.

本文介绍了信息检索的演进以及大语言模型和搜索引擎的利弊，提出了 InteR 框架，通过交互改进知识的检索，并通过实验验证其优越性。