人工智能语言模型中的植物盲和多样性
Anja Geitmann1, Amir J Bidhendi1
1Faculty of Agricultural and Environmental Sciences, McGill University, Montreal, QC, Canada.
Trends in plant science
|August 4, 2023
概括
大型语言模型 (LLM) 可以加快科学任务. 然而,聊天GPT.
科学领域:
- 科学研究中的人工智能
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 大型语言模型 (LLM) 显示了加速科学发现的潜力.
- 生物科学LLM的应用需要仔细评估.
研究的目的:
- 评估ChatGPT对生物问题的答案的多样性和可靠性.
- 突出了科学AI模型开发中专家验证的必要性.
主要方法:
- 分析了ChatGPT对一系列生物学相关查询的回应.
- 评估生成内容的一致性和准确性,重点关注特定的生物概念,如"植物意识".
主要成果:
- 关于生物主题,特别是"植物意识"的ChatGPT的回应显示出显著的变化.
- 这种变化凸显了当前LLM对复杂科学领域的理解的潜在局限性.
结论:
- 科学家必须积极验证人工智能生成的数据和培训方法.
- 确保人工智能工具的准确性和可靠性对于其有效地融入科学工作流程至关重要.
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