Improving Scientific Document Retrieval with Concept Coverage-based Query Set Generation

TL;DR

This paper presents CCQGen, a novel framework that generates concept-covered queries to improve scientific document retrieval, addressing the challenge of limited annotated data in specialized domains.

Contribution

The paper introduces a concept coverage-based query generation method that adaptively produces comprehensive queries, enhancing retrieval accuracy in scientific literature.

Findings

CCQGen improves query quality and coverage.

Enhanced retrieval performance demonstrated in experiments.

Adaptive query generation effectively covers document concepts.

Abstract

In specialized fields like the scientific domain, constructing large-scale human-annotated datasets poses a significant challenge due to the need for domain expertise. Recent methods have employed large language models to generate synthetic queries, which serve as proxies for actual user queries. However, they lack control over the content generated, often resulting in incomplete coverage of academic concepts in documents. We introduce Concept Coverage-based Query set Generation (CCQGen) framework, designed to generate a set of queries with comprehensive coverage of the document's concepts. A key distinction of CCQGen is that it adaptively adjusts the generation process based on the previously generated queries. We identify concepts not sufficiently covered by previous queries, and leverage them as conditions for subsequent query generation. This approach guides each new query to complement the previous ones, aiding in a thorough understanding of the document. Extensive experiments demonstrate that CCQGen significantly enhances query quality and retrieval performance.