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This chapter surveys 34 peer-reviewed studies that leverage generative large language models (LLMs) for literature retrieval and screening, addressing the challenges posed by the exponential growth of scholarly publications. By analyzing various methodologies, including model adaptation and prompting techniques, the research highlights how LLMs can streamline the identification of relevant studies against specific eligibility criteria. The findings underscore the potential of LLMs to enhance the efficiency of scientific knowledge discovery, offering a more flexible alternative to traditional search systems reliant on manual queries.
Generative LLMs can significantly reduce the manual effort required for literature retrieval and screening, transforming how researchers access scientific knowledge.
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.
