用大模型生成高质量科学综述，提升科研整合效率
LLMs4Synthesis: Leveraging Large Language Models for Scientific Synthesis
- 融合大模型与强化学习，自动整合科学文献
- 定义9项质量标准，评估综述的可靠性与深度
- 适合科研人员快速提炼领域进展，提升写作效率
针对科学文献数量激增与复杂性上升的问题，本文提出 LLMs4Synthesis 框架，旨在增强大语言模型（LLMs）生成高质量科学综述的能力。该框架支持开源与专有模型，实现快速、连贯且富含上下文的科学洞见整合，并评估综述的完整性与可靠性，弥补现有定量指标的不足。研究贡献包括：开发处理科学论文的新方法，定义新的综述类型，建立九项详细的评价标准。通过将大模型与强化学习及AI反馈结合，优化综述质量，确保符合既定准则。框架及其组件已公开，有望提升科研综述的生成与评估效率。
原文摘要 · Abstract (English)
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.
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