Knowledge Navigator: LLM-guided Browsing Framework for Exploratory Search in Scientific Literature

Uri Katz, Mosh Levy, Yoav Goldberg

Abstract
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.
Anthology ID:
2024.findings-emnlp.516
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2024
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8838–8855
Language:
URL:
https://preview.aclanthology.org/build-pipeline-with-new-library/2024.findings-emnlp.516/
DOI:
10.18653/v1/2024.findings-emnlp.516
Bibkey:
Cite (ACL):
Uri Katz, Mosh Levy, and Yoav Goldberg. 2024. Knowledge Navigator: LLM-guided Browsing Framework for Exploratory Search in Scientific Literature. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 8838–8855, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
Knowledge Navigator: LLM-guided Browsing Framework for Exploratory Search in Scientific Literature (Katz et al., Findings 2024)
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PDF:
https://preview.aclanthology.org/build-pipeline-with-new-library/2024.findings-emnlp.516.pdf
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