对比公开可用的大型语言模型的神经心理学知识库
Oscar R Kronenberger1, Matthew Hutnyan1, Alyssa N Kaser1
1Department of Psychiatry, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390-9044, USA.
概括
先进的人工智能模型在神经心理学中表现有前途,但需要谨慎. 思想链推理的大型语言模型 (LLM) 在实践问题上取得了更高的准确性,但仍然表现出知识差距和过度自信.
科学领域:
- 人工智能的人工智能
- 神经心理学 神经心理学
- 医学教育 医学教育
背景情况:
- 大型语言模型 (LLM) 越来越多地融入到各种专业领域.
- 评估LLM在神经心理学等专业领域的知识基础对于理解其潜力和局限性至关重要.
研究的目的:
- 评估开放式LLMs的神经心理学知识.
- 确定LLM在神经心理学中的潜在应用和局限性.
主要方法:
- 600个多选择的神经心理学问题被用于测试OpenAI (GPT-3.5,GPT-4,o3-mini-high) 和谷歌 (Gemini 1.0,2.0 FTE) 模型.
- 进行了两项测试试验,随后进行了统计分析,包括配对样本t测试和二项逻辑通用线性混合效应模型 (GLMMs).
- 顶级模特错过的问题被主题分析.
主要成果:
- 开放AI的o3-mini-high实现了最高的准确性 (90.3%在T2),其次是双子座2.0FTE (88.7%).
- 总体而言,LLM的准确性平均高估了15.8%.
- 思想链推理模型的表现优于旧模型,但在测试解释,诊断推理和神经解剖学方面发现了不准确性.
结论:
- 链式思维推理LLM在神经心理学教育,研究和实践中显示出潜在的实用性.
- 在特定的神经心理学内容领域的持续弱点和对错误答案的过度自信需要仔细解释LLM输出.
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