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

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Knowledge Navigator is a system for exploring scientific literature by organizing search results from broad queries into a structured hierarchy of topics and subtopics. This structure helps users navigate research themes, refine their focus, and discover more relevant documents. By combining LLMs with cluster-based methods, we enable an innovative browsing experience. We evaluate the system on the CLUSTREC-COVID and SCITOC benchmarks, and provide the code, prompts, and benchmarks publicly.

System Overview

Knowledge Navigator system takes a corpus of retrieved scientific documents for a given query and outputs an organized two-level thematic structure of subtopics spanning that topical query. The system's functionality is supported by the following conceptual steps: corpus construction, embedding and clustering of documents, describing and naming clusters, filtering irrelevant clusters, grouping the clusters into a thematic hierarchy, and subtopic query generation. This is implemented in a five-component architecture that largely follows the conceptual steps. This architecture enables LLMs to generate grounded outputs based on a large number of source documents, a crucial requirement for organizing and structuring large corpora.

Folders

• Navigator: This folder contains all the code necessary to run Knowledge Navigator locally.
• Benchmarks: This folder includes the CLUSTREC-COVID and SCITOC benchmarks used for evaluating the system.

Citation

Please cite our paper if you use this code in your own work:

@inproceedings{katz-etal-2024-knowledge,
    title = "Knowledge Navigator: {LLM}-guided Browsing Framework for Exploratory Search in Scientific Literature",
    author = "Katz, Uri  and
      Levy, Mosh  and
      Goldberg, Yoav",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.findings-emnlp.516",
    pages = "8838--8855",
}