The ability of Large Language Models (LLMs) to generate high-quality text and code has fuelled their rise in popularity. In this paper, we aim to demonstrate the potential of LLMs within the realm of optimization algorithms by integrating them into STNWeb. This is a web-based tool for the generation of Search Trajectory Networks (STNs), which are visualizations of optimization algorithm behavior. Although visualizations produced by STNWeb can be very informative for algorithm designers, they often require a certain level of prior knowledge to be interpreted. In an attempt to bridge this knowledge gap, we have incorporated LLMs, specifically GPT-4, into STNWeb to produce extensive written reports, complemented by automatically generated plots, thereby enhancing the user experience and reducing the barriers to the adoption of this tool by the research community. Moreover, our approach can be expanded to other tools from the optimization community, showcasing the versatility and potential of LLMs in this field.

翻译：大型语言模型（LLMs）生成高质量文本和代码的能力推动了其普及度的提升。本文旨在通过将LLMs集成到STNWeb中，展示其在优化算法领域的潜力。STNWeb是一款基于网络的工具，用于生成搜索轨迹网络（STNs）——即优化算法行为的可视化呈现。尽管STNWeb生成的可视化结果对算法设计者具有重要信息价值，但解读这些结果通常需要一定程度的先验知识。为弥合这一知识鸿沟，我们将LLMs（特别是GPT-4）融入STNWeb，使其能够生成详尽的书面报告，并辅以自动生成的图表，从而提升用户体验并降低研究社区采用该工具的门槛。此外，我们的方法可扩展至优化社区的其他工具，充分展示了LLMs在该领域的广泛适用性与潜力。