A Hybrid Approach to Literature-Based Discovery: Combining Traditional Methods with LLMs

We present a novel hybrid approach to literature-based discovery (LBD) which exploits large language models (LLMs) to enhance traditional LBD methodologies. We explore the use of LLMs to address significant LBD challenges: (1) the extraction of factual subject–predicate–object relations from publication abstracts using few-shot learning and (2) the filtering of unpromising candidate hidden knowledge pairs (CHKPs) using a variant of the LLM-as-a-judge paradigm with and without the addition of domain-specific information using retrieval augmented generation. The approach produces relations with greater coverage and results in a lower number of CHKPs compared to LBD based on relations extracted with, e.g., SemRep, improving the prediction and efficiency of knowledge discovery. We demonstrate the utility of the method using a drug-repurposing case study and suggest that emerging AI technologies can be used to assist in knowledge discovery from the ever-growing volume of the scientific literature.