Knowledge Navigator: LLM-guided Browsing Framework for Exploratory Search in Scientific Literature
This addresses the challenge of managing exponential growth in scientific literature for researchers, though it appears incremental as it combines existing LLM and cluster-based methods.
The paper tackles the problem of exploring scientific literature by introducing Knowledge Navigator, a system that organizes documents into a navigable hierarchy of topics and subtopics, enabling iterative search and deeper discovery, and demonstrates its effectiveness through evaluations on two novel benchmarks.
The exponential growth of scientific literature necessitates advanced tools for effective knowledge exploration. We present Knowledge Navigator, a system designed to enhance exploratory search abilities by organizing and structuring the retrieved documents from broad topical queries into a navigable, two-level hierarchy of named and descriptive scientific topics and subtopics. This structured organization provides an overall view of the research themes in a domain, while also enabling iterative search and deeper knowledge discovery within specific subtopics by allowing users to refine their focus and retrieve additional relevant documents. Knowledge Navigator combines LLM capabilities with cluster-based methods to enable an effective browsing method. We demonstrate our approach's effectiveness through automatic and manual evaluations on two novel benchmarks, CLUSTREC-COVID and SCITOC. Our code, prompts, and benchmarks are made publicly available.