There is an increasing interest in leveraging Large Language Models (LLMs) for managing structured data and enhancing data science processes. Despite the potential benefits, this integration poses significant questions regarding their reliability and decision-making methodologies. It highlights the importance of various factors in the model selection process, including the nature of the data, problem type, performance metrics, computational resources, interpretability vs accuracy, assumptions about data, and ethical considerations. Our objective is to elucidate and express the factors and assumptions guiding GPT-4's model selection recommendations. We employ a variability model to depict these factors and use toy datasets to evaluate both the model and the implementation of the identified heuristics. By contrasting these outcomes with heuristics from other platforms, our aim is to determine the effectiveness and distinctiveness of GPT-4's methodology. This research is committed to advancing our comprehension of AI decision-making processes, especially in the realm of model selection within data science. Our efforts are directed towards creating AI systems that are more transparent and comprehensible, contributing to a more responsible and efficient practice in data science.

翻译：利用大型语言模型（LLMs）管理结构化数据并增强数据科学流程的兴趣日益增长。尽管具有潜在优势，这种整合对其可靠性和决策方法论提出了重大质疑。它凸显了模型选择过程中多种因素的重要性，包括数据性质、问题类型、性能指标、计算资源、可解释性与准确性权衡、数据假设以及伦理考量。我们的目标是阐明并表达指导GPT-4模型选择建议的因素与假设。我们采用可变性模型描述这些因素，并利用玩具数据集评估模型及所识别启发式方法的实现。通过将这些结果与其他平台的启发式方法进行对比，我们旨在确定GPT-4方法论的有效性与独特性。本研究致力于增进对AI决策过程的理解，尤其是在数据科学中的模型选择领域。我们的努力旨在创建更透明、可理解的AI系统，推动数据科学中更负责任、更高效的实践。