在大型多式联络模型和人类中比较感知判断
Billy Dickson1, Sahaj Singh Maini2, Craig Sanders3
1Department of Computer Science, Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington, 700 N Woodlawn Ave, Bloomington, IN, 47408, USA. dicksonb@iu.edu.
Behavior research methods
|June 19, 2025
概括
大型多式联络模型 (LMM) 在认知科学研究中有望取代人类判断. GPT-4o与人类对岩石图像的看法非常相匹配,特别是在基本特征方面,提供了更快的替代方案.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 认知科学家使用人类对刺激的判断来构建注意力,记忆,学习和决策的模型.
- 收集这些感知判断往往是耗时和昂贵的.
- 大型多式模式 (LMM) 提供了一个潜在的解决方案,因为它们能够处理文本和图像.
研究的目的:
- 评估LMMs作为取代人类参与者收集感知判断的实用性.
- 在认知科学中使用的岩石图像的标准化数据集上评估各种LMM的性能.
主要方法:
- 在岩石图像数据集上对人类感知判断进行评估的LMM判断.
- 数据集包括人类对岩石分类至关重要的10个维度的评分.
- 对比LMM性能,重点关注GPT-4o与人类反应的相关性.
主要成果:
- GPT-4o显示了与人类判断的最高正相关性.
- GPT-4o显示出与基本感知维度 (轻度,色度,亮度,纹理) 的人类评级有很强的对齐.
- 对于抽象的,岩石特定的维度 (组织,岩结构) 相关性较低.
结论:
- LMMs,特别是GPT-4o,显示出在感知判断任务中取代人类参与者的潜力.
- 当前的LMM对于基本的感知特征接近人类的共识,尽管抽象的特征需要进一步发展.
- 这项研究建立了一个基准,用于使用人类感知数据评估未来的LMM.
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