LLMs4Synthesis: Leveraging Large Language Models for Scientific Synthesis
Hamed Babaei Giglou, Jennifer D’Souza, Sören Auer · 2024
In response to the growing complexity and volume of scientific literature, this paper introduces the LLMs4Synthesis framework, designed to enhance the capabilities of Large Language Models (LLMs) to synthesize the key insights from scientific texts as high-quality and concise summaries. This framework addresses the need for rapid, coherent, and contextually rich integration of key scientific insights, leveraging both open-source and proprietary LLMs. It also examines the effectiveness of LLMs in evaluating the integrity and reliability of these syntheses, alleviating inadequacies in current quantitative metrics. The contributions of this study are a novel methodology for synthesizing key scientific insights, definition of new synthesis types, and establishing nine detailed quality criteria for evaluating syntheses. The implementation fits LLMs with reinforcement learning to optimize for synthesis quality by alignment with our established quality criteria. The LLMs4Synthesis framework and its components are available, promising to improve the generation and evaluation of scientific research synthesis.