在思维理论中评估大型语言模型的任务任务
1Graduate School of Business, Stanford University, Stanford, CA 94305.
概括
大型语言模型 (LLM) 显示出新兴的思维理论 (ToM) 等能力. 聊天GPT-4在错误信念任务中实现了75%的准确性,与幼儿匹配,这表明AI可能与语言一起发展社会技能.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 发展心理学 发展心理学
背景情况:
- 思想理论 (ToM) 对人类社会互动至关重要.
- LLM 是越来越复杂的人工智能系统.
- 在AI中评估ToM是一个新的研究领域.
研究的目的:
- 评估大语言模型 (LLM) 的思维理论 (ToM) 能力.
- 将LLM在ToM任务上的表现与人类发展基准进行比较.
- 探索AI中新兴的ToM类能力的含义.
主要方法:
- 11个LLM在40个定制的错误信念任务中进行了测试.
- 任务包括错误的信念,真实的信念和逆转的场景.
- 绩效是通过所有八种场景类型中正确解决的任务数量来衡量的.
主要成果:
- 旧的LLM在所有任务中都失败了.
- GPT-3和ChatGPT-3.5-turbo实现了20%的准确性.
- 聊天GPT-4的准确率达到75%,相当于6岁儿童的准确率.
结论:
- 法学士证明了新兴的类似于ToM的能力.
- 这些能力可能是AI中高级语言技能的新兴属性.
- 社会熟练的人工智能的发展具有重大影响.
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