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Information retrieval (IR) plays a crucial role in locating relevantresources from vast amounts of data, and its applications have evolved fromtraditional knowledge bases to modern retrieval models (RMs). The emergence oflarge language models (LLMs) has further revolutionized the IR field byenabling users to interact with search systems in natural languages. In thispaper, we explore the advantages and disadvantages of LLMs and RMs,highlighting their respective strengths in understanding user-issued queriesand retrieving up-to-date information. To leverage the benefits of bothparadigms while circumventing their limitations, we propose InteR, a novelframework that facilitates information refinement through synergy between RMsand LLMs. InteR allows RMs to expand knowledge in queries using LLM-generatedknowledge collections and enables LLMs to enhance prompt formulation usingretrieved documents. This iterative refinement process augments the inputs ofRMs and LLMs, leading to more accurate retrieval. Experiments on large-scaleretrieval benchmarks involving web search and low-resource retrieval tasksdemonstrate that InteR achieves overall superior zero-shot retrievalperformance compared to state-of-the-art methods, even those using relevancejudgment. Source code is available at https://github.com/Cyril-JZ/InteR

Synergistic Interplay between Search and Large Language Models for Information Retrieval