Scientific Knowledge Discovery in the Age of Large Language Models

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.

Digital Libraries (cs.DL); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR)

arXiv:2607.26670 [cs.DL]

(or arXiv:2607.26670v1 [cs.DL] for this version)

https://doi.org/10.48550/arXiv.2607.26670 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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