Scientific Knowledge Discovery in the Age of Large Language Models

2026-07-29

AI Summary

This study addresses the inefficiency of traditional literature retrieval methods, which rely heavily on manually crafted queries and screening, in the face of exponentially growing academic publications. The authors present a systematic review of 34 peer-reviewed studies that apply generative large language models (LLMs) to scientific literature retrieval and screening, offering the first comprehensive mapping of their application paradigms in scientific knowledge discovery. By leveraging Boolean queries on the OpenAIRE Graph and analyzing approaches through the lenses of prompt engineering, model adaptation, and architectural design, the work identifies key technical pathways and evaluation frameworks. Beyond structuring the current landscape, the study highlights the substantial potential of LLMs to enhance the automation and efficiency of scientific discovery, providing a systematic reference for future research.

Abstract

The rapid growth of scholarly literature has made identifying relevant publications increasingly difficult, and conventional search systems still depend heavily on manually formulated queries and effortful manual inspection. Generative large language models (LLMs) offer a more flexible alternative, supporting literature retrieval and the screening of candidate studies against eligibility criteria. This chapter surveys 34 peer-reviewed papers applying generative LLMs to these two tasks, identified via a Boolean search over the OpenAIRE Graph (1,589 records screened to 34 inclusions). Reviewed studies are characterised by LLMs employed, model access and adaptation, prompting and architectural techniques, ground-truth sources, and evaluation metrics.

Problem

• scientific knowledge discovery
• literature retrieval
• study screening
• large language models
• scholarly literature

Innovation

• large language models
• scientific literature retrieval
• study screening
• prompt engineering
• knowledge discovery