Literature-based Hypothesis Generation using Large Language Models

Abstract

Literature-based discovery (LBD) aims to uncover non-obvious connections between concepts across the scientific literature. This paper investigates how large language models can be used to automate and scale hypothesis generation in this framework, moving beyond keyword matching to reasoning over structured evidence across papers.

Represents my current research direction: AI systems that accelerate discovery workflows.