在使用ChatGPT的大型语言模型中,具有类似人类问题的解决能力
Graziella Orrù1, Andrea Piarulli1, Ciro Conversano1
1Department of Surgical, Medical, Molecular and Critical Area Pathology, University of Pisa, Pisa, Italy.
Frontiers in artificial intelligence
|June 9, 2023
概括
这项研究表明,ChatGPT是一种生成预训练变压器 (GPT) 模型,可以有效地解决口头洞察力问题,匹配人类的表现. 它的先进架构支持其在复杂的问题解决任务中的潜力.
科学领域:
- 人工智能的人工智能
- 认知科学 认知科学
- 心理学 心理学 心理学
背景情况:
- 最近机器学习 (ML) 的进展,特别是生成预训练变压器 (GPT) 模型,对人工智能 (AI) 产生了重大影响.
- 包括基于聊天的变体在内的GPT模型在语言处理任务中表现出前所未有的准确性.
研究的目的:
- 评估ChatGPT在口头洞察问题上的解决问题能力.
- 将ChatGPT的表现与已建立的人类性能基准进行比较.
主要方法:
- 聊天GPT接受了30个口头洞察力问题,分为练习和转移集.
- 绩效被分为二进制 (1为正确,0为不正确),每组最高可获得15分.
- 将ChatGPT的溶解率与人类样本 (20个受试者) 进行了比较.
主要成果:
- 聊天GPT在开拓思维和解决口头洞察力问题方面表现出潜力.
- 它的整体表现与实践和转移问题的最可能的人类结果相匹配.
- 在聚合的问题集中,ChatGPT的解决方案在人类结果中排在前5%的位置.
结论:
- 聊天GPT的变压器架构和自我注意机制可能有助于其解决问题的能力.
- 这些发现强调了将人工智能整合到心理学研究中的潜力,以了解认知过程.
- 进一步的研究是必要的,以充分阐明AI在口头解决问题的能力和局限性.
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