PDF(Pubmed)
Abstract：
Large language models (LLMs) have emerged as a powerful tool for biomedical researchers, demonstrating remarkable capabilities in understanding and generating human-like text. ChatGPT with its Code Interpreter functionality, an LLM connected with the ability to write and execute code, streamlines data analysis workflows by enabling natural language interactions. Using materials from a previously published tutorial, similar analyses can be performed through conversational interactions with the chatbot, covering data loading and exploration, model development and comparison, permutation feature importance, partial dependence plots, and additional analyses and recommendations. The findings highlight the significant potential of LLMs in assisting researchers with data analysis tasks, allowing them to focus on higher-level aspects of their work. However, there are limitations and potential concerns associated with the use of LLMs, such as the importance of critical thinking, privacy, security, and equitable access to these tools. As LLMs continue to improve and integrate with available tools, data science may experience a transformation similar to the shift from manual to automatic transmission in driving. The advancements in LLMs call for considering the future directions of data science and its education, ensuring that the benefits of these powerful tools are utilized with proper human supervision and responsibility.
摘要：
大型语言模型（LLM）已经成为生物医学研究人员的强大工具，在理解和生成类似人类的文本方面表现出非凡的能力。ChatGPT及其代码解释器功能，具有编写和执行代码能力的LLM，通过启用自然语言交互，简化数据分析工作流程。使用以前发布的教程中的材料，类似的分析可以通过与聊天机器人的会话交互来执行，涵盖数据加载和探索，模型开发和比较，排列特征重要性，部分依赖图，以及其他分析和建议。这些发现强调了LLM在协助研究人员完成数据分析任务方面的巨大潜力，让他们专注于更高层次的工作。然而,存在与使用LLM相关的限制和潜在问题，比如批判性思维的重要性，隐私,安全,以及公平使用这些工具。随着LLM不断改进并与可用工具集成，数据科学可能会经历类似于驾驶中从手动变速器到自动变速器的转变。LLM的进步要求考虑数据科学及其教育的未来方向，确保在适当的人力监督和责任下利用这些强大工具的好处。
