大型语言模型中的 (非) 理性和认知偏见
Olivia Macmillan-Scott1, Mirco Musolesi1,2
1Department of Computer Science, University College London, London, UK.
Royal Society open science
|August 5, 2024
概括
大型语言模型 (LLM) 在推理任务中表现出非理性,类似于人类. 然而,它们的错误和不一致性不同于人类的偏见,表明了模型的独特局限性.
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:
- 众所周知,大型语言模型 (LLM) 从训练数据中继承人类偏见.
- 在何种程度上LLM表现出理性推理,以及它如何与人类理性相比,尚不清楚.
研究的目的:
- 调查大型语言模型 (LLM) 是否显示合理推理.
- 使用已确定的心理任务,将LLM的推理模式与人类认知偏见进行比较.
主要方法:
- 评估了七种不同的大型语言模型 (LLM).
- 在认知心理学文献中常见的任务用于评估推理.
- 分析了LLM答案中的错误和不一致性.
主要成果:
- LLM在推理任务中表现出非理性,在某些方面反映了人类的表现.
- 在LLM中,非理性表现与人类认知偏见不同,错误往往是模型特有的.
- LLM 显示出显著的响应不一致性,增加了另一层非理性.
结论:
- 在推理任务中,LLM不会复制人类的非理性.
- 法学士推理的特点是独特的错误模式和显著的响应不一致性.
- 该研究提出了评估和比较LLM能力的方法框架,特别是理性推理.
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