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) in generating high-quality scientific syntheses. This framework addresses the need for rapid, coherent, and contextually rich integration of 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. Our study contributes to this field by developing a novel methodology for processing scientific papers, defining new synthesis types, and establishing nine detailed quality criteria for evaluating syntheses. The integration of LLMs with reinforcement learning and AI feedback is proposed to optimize synthesis quality, ensuring alignment with established criteria. The LLMs4Synthesis framework and its components are made available, promising to enhance both the generation and evaluation processes in scientific research synthesis.

翻译：针对科学文献日益增长的复杂性和体量，本文提出了LLMs4Synthesis框架，旨在增强大型语言模型（LLMs）生成高质量科学综述的能力。该框架利用开源和专有LLMs，旨在满足快速、连贯且上下文丰富地整合科学见解的需求。同时，本文研究了LLMs在评估这些综述的完整性和可靠性方面的有效性，以弥补当前定量指标的不足。我们的研究通过开发一种处理科学论文的新方法、定义新的综述类型，并建立九个详细的评估综述的质量标准，为该领域做出了贡献。我们提出将LLMs与强化学习和AI反馈相结合，以优化综述质量，确保其符合既定标准。LLMs4Synthesis框架及其组件已公开可用，有望提升科学研究中综述的生成与评估过程。