数据科学通过自然语言与ChatGPT的代码解释器
Sangzin Ahn1,2
1Department of Pharmacology and Pharmacogenomics Research Center, Inje University College of Medicine, Busan 47392, Korea.
Translational and clinical pharmacology
|July 8, 2024
概括
大型语言模型 (LLM) 通过自然语言交互简化生物医学数据分析. 这些人工智能工具为研究人员提供了巨大的潜力,但需要对隐私和访问进行严格的监督.
科学领域:
- 生物医学研究生物医学研究
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 大型语言模型 (LLM) 展示了先进的文本理解和生成能力.
- 聊天GPT的代码解释器将自然语言交互与代码执行集成为数据分析.
研究的目的:
- 探索LLM的实用性,特别是与代码解释器的ChatGPT,以简化生物医学数据分析工作流程.
- 评估对话式人工智能的潜力,帮助研究人员完成从数据加载到高级模型解释等任务.
主要方法:
- 从之前的教程中利用材料,通过使用ChatGPT的自然语言提示来执行数据分析.
- 涵盖了关键的数据科学步骤,包括数据加载,探索,模型开发,排列的重要性和部分依赖图.
主要成果:
- 证明了LLM可以通过对话互动有效地执行复杂的数据分析任务.
- 通过LLM的协助,研究人员可以专注于更高层次的科学调查和解释.
结论:
- 在转变数据科学工作流程和支持生物医学研究方面,LLM显著有前途.
- 关键考虑因素包括确保负责任的使用,解决隐私和安全问题,并促进对这些人工智能工具的公平访问.
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