Automated literature research and review generation method based on large language models

Abstract Literature research, which is vital for scientific work, faces the challenge of surging information volumes that are exceeding researchers’ processing capabilities. This paper describes an automated review-generation method based on large language models (LLMs) to overcome efficiency bottlenecks and reduce cognitive load. Our statistically validated evaluation framework demonstrates that the generated reviews match or exceed manual quality, offering broad applicability across research fields without requiring user domain knowledge. Applied to propane dehydrogenation catalysts, our method demonstrated two aspects: first, generating comprehensive reviews from 343 articles spanning 35 topics; and, second, evaluating data-mining capabilities by using 1041 articles for experimental catalyst property analysis. Through multilayered quality control, we effectively mitigated the hallucinations of LLMs, with expert verification confirming accuracy and citation integrity, while demonstrating hallucination risks reduced to <0.5% with 95% confidence. The released software application enables one-click review generation, enhancing research productivity and literature-recommendation efficiency while facilitating broader scientific explorations.