超越任何可想象的机制的局限性:大型语言模型和心理语言学
Conor Houghton1, Nina Kazanina2,3, Priyanka Sukumaran1,2
1Department of Computer Science, University of Bristol, Bristol, UK conor.houghton@bristol.ac.uk p.sukumaran@bristol.ac.uk conorhoughton.github.io.
The Behavioral and brain sciences
|December 6, 2023
概括
大型语言模型 (LLM) 提供了一个有效的语言模型,虽然不是一个详细的人类语言处理器. 它们是心理学语言学的宝贵工具,用于实际应用,比较和重新思考语言和思想联系.
科学领域:
- 心理语言学 心理语言学
- 计算语言学 计算语言学
- 认知科学 认知科学
背景情况:
- 大型语言模型 (LLM) 在自然语言处理方面表现出了显著的能力.
- 现有的人类语言处理模型是复杂的,可能无法完全捕捉新出现的LLM行为.
- 人工智能和认知科学的交集需要对LLM相关性的审查.
研究的目的:
- 在心理语言学领域评估大语言模型 (LLM) 的实用性.
- 探索LLM作为语言理解的实用工具和比较基准.
- 考虑LLM对语言和认知理论的哲学含义.
主要方法:
- 与心理语言学理论相关的LLM能力的概念分析.
- 审查目前的应用和在语言研究LLMs的潜在用途.
- 关于语言建模及其认知相关的本质的哲学调查.
主要成果:
- 语言学士擅长模拟语言输出,但不能复制详细的人类语言处理机制.
- 语言学习法作为语言任务的有效实用工具,并作为有价值的比较模型.
- 语言学士课程为讨论语言与思想之间的关系提供了一个新的框架.
结论:
- 大型语言模型 (LLM) 对心理语言学具有重要意义,因为它们的实用实用性和比较价值.
- 语言学士课程促使人们重新评估语言,思想和人工智能之间的基本关系.
- 需要进一步的研究来将LLM的见解融入认知和语言理论中.
相关概念视频
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