在对话性AI中关于流行病学关联的因果推理
1Cox Associates, MoirAI, Entanglement, and University of Colorado, 503 N. Franklin Street, Denver, CO 80218, USA.
Global epidemiology
|August 28, 2023
概括
对ChatGPT的索克拉斯质疑显示,虽然细颗粒物 (PM2.5) 与死亡率有很强的关联,但其因果关系仍然不确定. 需要进一步的调查来完善LLM的推理和改善健康风险评估.
科学领域:
- 流行病学 流行病学
- 人工智能的人工智能
- 公共卫生 公共卫生
背景情况:
- 流行病学研究经常报告细颗粒物 (PM2.5) 与增加人类死亡风险之间的关联.
- 像ChatGPT这样的大型语言模型 (LLM) 可以反映其训练数据中发现的人类推理模式.
- 对环境健康数据的因果解释对公共卫生政策至关重要.
研究的目的:
- 通过与ChatGPT进行苏格拉底式对话,探索PM2.5和死亡风险之间的流行病学关联的因果解释.
- 评估法学士的批判性评估科学证据的能力,并通过询问来完善其结论.
- 评估LLMs在改善科学推理和初步结论可靠性的潜力.
主要方法:
- 采用了苏格拉底式对话方法,以问答形式参与了ChatGPT.
- 对话的重点是解释证据,将PM2.5暴露与人类死亡率联系起来.
- 分析了ChatGPT的反应,以寻找推理和结论确定性的变化.
主要成果:
- 最初,ChatGPT声称PM2.5和死亡率之间的确立联系,强调公共卫生的重要性.
- 通过持续的询问,ChatGPT修订了其立场,得出结论,由于潜在的遗漏混因素,因果关系是不确定的.
- 这表明,在被要求时,LLM有能力改进其对证据的解释.
结论:
- 持续的苏格拉底式质疑可以改善LLMs模仿的推理和论证.
- 在最初的过程中,LLM可能会提出强有力的结论,但需要进一步审问以获得细微的理解.
- 在科学解释中,LLM产生的结论的可靠性可以通过交互式对话来提高.
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