聊天GPT有典型或非典型的思维理论吗?
Margherita Attanasio1, Monica Mazza1,2, Ilenia Le Donne1
1Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy.
Frontiers in psychology
|November 13, 2024
概括
像ChatGPT这样的大型语言模型显示出先进的思维理论 (ToM) 能力,但与复杂的情绪作斗争. 他们的多言多语风格类似于自闭症谱系障碍 (ASD) 患者的风格.
科学领域:
- 认知心理学 认知心理学
- 人工智能的人工智能
- 计算语言学 计算语言学
背景情况:
- 大型语言模型 (LLM) 展示了类似人类的行为模式,引发了对实验心理学的兴趣.
- 心理理论 (ToM) 障碍与自闭症谱系障碍 (ASD) 等疾病中的社会困难有关.
- 以前的研究表明,LLM可以通过基本的ToM任务,但高级的ToM响应风格仍然未被探索.
研究的目的:
- 调查ChatGPT 3.5和ChatGPT-4的高级思维理论 (ToM) 和情感归因能力.
- 将复杂的ToM任务的LLM绩效与典型发育 (TD) 和自闭症谱系障碍 (ASD) 组进行比较.
- 分析与临床人群相关的LLM的对话风格.
主要方法:
- 将高级ToM测试和情绪归因任务管理到ChatGPT 3.5和ChatGPT-4.
- 将LLM反应与人类参与者 (ASD和TD组) 的数据进行比较.
- 对LLM响应风格的定性分析,重点关注多言多语和遵守对话的最大原则.
主要成果:
- 两种ChatGPT版本都在理解心理状态方面表现出很高的准确性,尽管ChatGPT-3.5在处理更复杂的状态方面遇到了困难.
- 与TD组相比,ChatGPT-3.5在情绪归因方面表现较差,类似于ASD组,尤其是负面情绪.
- 聊天GPT-4表现出更高的准确性,但仍然面临挑战,识别特定的情绪,如悲伤和愤怒.
- 伦理学士的反应风格是多言多语和重复的,反映了在高功能自闭症个体中观察到的对话模式.
结论:
- 虽然不完美,但LLM拥有显著的高级ToM和情感识别能力.
- 法律学士的对话风格可能与具有高功能自闭症的个体有共同的特征.
- 研究结果表明,LLM在理解社会认知和相关障碍方面具有潜在的临床影响和应用.
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