大型语言模型对于理解人类语言和认知的局限性
Christine Cuskley1, Rebecca Woods1, Molly Flaherty2
1Language Evolution, Acquisition and Development Group, Newcastle University, Newcastle upon Tyne, UK.
Open mind : discoveries in cognitive science
|September 4, 2024
概括
大型语言模型 (LLM) 对人类语言的获取和演变提供了有限的见解. 它们的基于文本的处理从根本上不同于丰富的,多式模式的人类学习,限制了它们作为研究工具的实用性.
科学领域:
- 认知科学 认知科学
- 计算语言学 计算语言学
- 进化生物学 进化生物学
背景情况:
- 大型语言模型 (LLM) 越来越多地被提出作为研究人类语言发展和演变的工具.
- 关于学习平衡与语言获取中的先天因素的争论仍在继续.
研究的目的:
- 批判性地评估LLM可以在多大程度上帮助我们理解人类语言的获取和演变.
- 区分LLM与人类语言之间的功能相似性和机制差异.
主要方法:
- 对LLM处理 (基于文本) 与人类语言获取 (多模式,交互) 的比较分析.
- 应用伦理学"四个问题"框架来评估LLM能力.
- 检查LLMs在代表人类语言行为的广度方面的范围和局限性.
主要成果:
- 在LLM输出和人类语言之间的相似之处是表面和功能性的,而不是机械的.
- 与人类丰富的多模式输入不同,LLM主要处理大量的单模式文本.
- 法学士的模仿写作的设计限制了他们对自然主义互动和理解各种人类语言的能力.
结论:
- 单独的LLM只能提供对人类语言获取和演变的最小洞察力.
- 法律法规是工具,而不是理论;它们的有效使用需要特定的研究假设.
- 理解人类语言需要承认当前的LLM架构的局限性.
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