在大型语言模型中出现的模拟推理
Taylor Webb1, Keith J Holyoak2, Hongjing Lu2,3
1Department of Psychology, University of California, Los Angeles, CA, USA. taylor.w.webb@gmail.com.
Nature human behaviour
|July 31, 2023
概括
像GPT-3这样的大型语言模型展示了新兴的模拟推理,在没有特殊培训的情况下与人类在新问题上的表现相匹配. 这表明,如果有足够的数据,人工智能可能会产生高级认知能力.
科学领域:
- 认知科学 认知科学
- 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) 中类似人类认知能力的出现是持续辩论的主题.
- 零射击推理,即在没有直接培训的情况下解决新问题的能力,是关键的兴趣领域.
- 类似推理是人类解决问题和理解新情况的基础.
研究的目的:
- 直接比较人类的模拟推理能力和特定的大型语言模型 (GPT-3).
- 调查法学士的抽象模式诱导和零射击问题解决能力.
- 评估LLM是否表现出与人类类比推理相似的新兴认知能力.
主要方法:
- 一项涉及人类参与者和文本-达芬奇-003变体的生成预训练变压器 (GPT) -3的比较研究.
- 使用了一系列模拟推理任务,包括一个非视觉矩阵推理任务,反映了雷文的标准渐进矩阵.
- 在需要抽象模式诱导的零射击任务上评估性能.
主要成果:
- 在模拟任务中,GPT-3表现出强大的抽象模式诱导能力.
- 在大多数测试场景中,GPT-3的性能与人类的能力相匹配或超过.
- 预先对GPT-4的评估表明,在模拟推理方面,它的性能甚至更好.
结论:
- 大型语言模型,以GPT-3为例,具有对零射击模拟推理的新兴能力.
- 这些发现表明,LLM可以获得复杂的解决问题的技能,而不需要明确的特定任务培训.
- 该研究强调了人工智能开发通用推理能力的潜力.
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