AI Papers Reader

Personalized digests of latest AI research

View on GitHub

Knowledge Navigator: LLM-guided Browsing Framework for Scientific Literature

The explosion in scientific publications presents a significant challenge for researchers seeking to effectively explore and synthesize vast amounts of information. Traditional search engines, while adept at retrieving specific documents, often fall short when handling broad, topical queries. They deliver lengthy ranked lists of potentially relevant papers, overwhelming researchers with an information overload and obscuring the underlying structure of the topic. Knowledge Navigator, a new framework proposed by Uri Katz, Mosh Levy, and Yoav Goldberg, leverages large language models (LLMs) to address this challenge, organizing scientific literature into a navigable, hierarchical structure of subtopics.

Imagine searching for information on “Tool Use in Animals”. Traditional search results would likely yield a long list of papers, making it difficult to grasp the full breadth of the topic and discover relevant subtopics. Knowledge Navigator takes this vast corpus of retrieved documents and structures it into a two-level hierarchy. The first level identifies broad themes, such as “Neural Mechanisms and Cognitive Processes” or “Comparative and Evolutionary Perspectives.” Each theme is then further broken down into specific subtopics like “Neural Mechanisms in Humans and Animals” or “Tool Use in Early Hominin Evolution”. This hierarchical organization allows users to gain a comprehensive understanding of the research themes within the domain while also enabling them to focus on specific subtopics of interest.

Knowledge Navigator leverages the capabilities of LLMs, which are incredibly effective at consuming information, but can be bottlenecked by the amount of information they can process at once. To address this, Knowledge Navigator implements a bottom-up approach, progressively abstracting information at each stage. It starts by clustering the retrieved documents into smaller groups based on their content, then uses an LLM-based component to analyze each group, identify common themes, and assign a meaningful name and description. These names and descriptions are then further organized into a hierarchical structure, reflecting the overall topic.

To evaluate the effectiveness of Knowledge Navigator, the authors conducted comprehensive experiments using two novel benchmarks, CLUSTREC-COVID and SCITOC. CLUSTREC-COVID is a modified version of the TREC-COVID benchmark, specifically designed for subtopic clustering, cluster-based aspect generation, and query generation tasks. SCITOC is a new dataset constructed from scientific review tables of contents, encompassing a wide range of scientific fields. Using these benchmarks, the authors demonstrated the ability of Knowledge Navigator to accurately identify subtopics, name them meaningfully, and filter out irrelevant clusters. They also showed that the system can generate effective queries for expanding specific subtopics, enabling deeper exploration without requiring manual query curation.

The findings suggest that Knowledge Navigator holds immense potential for transforming the way researchers engage with scientific literature. By organizing information into a structured and easily navigable framework, it empowers researchers to quickly grasp the broader research themes, identify areas of interest, and uncover novel connections within a given topic. Knowledge Navigator could be particularly useful for scientists working in rapidly evolving fields, where staying abreast of the latest developments is crucial. As LLMs continue to advance, Knowledge Navigator has the potential to become an essential tool for researchers across various disciplines.

The authors have made their code, prompts, and benchmarks publicly available, paving the way for further research and development in this promising area. The future of scientific literature exploration might just be guided by intelligent browsing systems like Knowledge Navigator, allowing researchers to navigate the vast sea of information with unprecedented ease and efficiency.