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语言大写:LLMs,ChatGPT,意思和理解
1Department of Psychology, University of Montreal, Montreal, QC, Canada.
Frontiers in artificial intelligence
|February 27, 2025
概括
像ChatGPT这样的大型语言模型 (LLM) 由于固有的语言偏见而表现出令人惊的功能,而不是真正的理解. 这些与语言结构和缺乏感官运动接地相关的偏见解释了它们的高级性能.
科学领域:
- 人工智能的人工智能
- 计算语言学 计算语言学
- 认知科学 认知科学
背景情况:
- 像ChatGPT这样的大型语言模型 (LLM) 展示了非凡的能力,尽管已知的潜在机制 (例如,庞大的数据库,统计学习) 仍然让研究人员感到惊.
- 他们的能力的程度使一些人质疑LLM是否具有真正的理解,这项工作驳斥了这一观念.
- 相反,LLM高性能的确切原因仍然不清楚,这促使人们对新出现性质进行调查.
研究的目的:
- 提出解释大规模LLMs意想不到的成功的假设.
- 识别特定的,良性"偏见"或语言固有的融合约束,有助于LLM的表现.
- 探索这些语言偏见和在LLMs中缺乏直接的感觉运动接地之间的关系.
主要方法:
- 这项研究表明,有几种趋同偏见可能解释LLM能力.
- 这些包括间接的语言接地对直接的感觉运动接地的寄生.
- 其他被认为的偏见包括口头定义循环性,在语言处理中反映,命题标志性和分类感知的计算类型.
主要成果:
- 该研究认为,LLM的成功源于语言在规模上的固有特性,而不是真正的理解.
- 这些属性作为"偏差"或约束,指导模型的输出.
- 发现的偏见与缺乏直接的感官运动接地本质上有关,这使得LLM无法将单词与现实世界的参考联系起来.
结论:
- 由于缺乏感官运动接地,LLM不能理解人类意义上的语言.
- 它们的先进性能归因于在规模上出现的语言偏见,这是语言本身固有的.
- 这些偏见,如间接的口头接地和定义循环,为理解LLM能力提供了一个框架,而不需要真正的理解.
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