知識導航器：LLM 引導的科學文獻探索式瀏覽框架

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

August 28, 2024

作者: Uri Katz, Mosh Levy, Yoav Goldberg

cs.AI

摘要

科學文獻的指數增長需要先進的工具來進行有效的知識探索。我們提出了知識導航器（Knowledge Navigator），這是一個旨在通過將廣泛主題查詢檢索的文檔組織和結構化為可導航的兩級命名和描述性科學主題和子主題層次結構，以增強探索性搜索能力的系統。這種結構化組織提供了一個領域研究主題的整體視圖，同時還允許用戶通過細化焦點並檢索額外相關文檔，在特定子主題中進行迭代搜索和更深入的知識發現。知識導航器結合了LLM功能和基於集群的方法，以實現一種有效的瀏覽方法。我們通過對兩個新穎基準CLUSTREC-COVID和SCITOC進行自動和手動評估展示了我們方法的有效性。我們的代碼、提示和基準已公開提供。

English

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.