知识导航器：LLM引导的科学文献探索搜索框架

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

随着科学文献的指数增长，需要先进的工具来进行有效的知识探索。我们提出了知识导航器（Knowledge Navigator），这是一个旨在通过将广泛主题查询检索到的文档组织和结构化为可导航的两级命名和描述性科学主题和子主题层次结构，以增强探索性搜索能力的系统。这种结构化组织提供了领域内研究主题的整体视图，同时还通过允许用户细化焦点并检索到额外相关文档，使用户能够在特定子主题内进行迭代搜索和深入知识发现。知识导航器结合了LLM能力和基于聚类的方法，以实现一种有效的浏览方法。我们通过对两个新颖基准数据集CLUSTREC-COVID和SCITOC进行自动和手动评估，展示了我们方法的有效性。我们的代码、提示和基准数据集已公开提供。

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.