大型语言模型中的符号和接地
1Department of Computer Science, Brown University, Providence, RI, USA.
概括
大型语言模型 (LLM) 显示出对理解人类语言的希望. 需要进一步的经验研究来探索它们的潜在认知过程和表征.
科学领域:
- 人工智能的人工智能
- 认知科学 认知科学
- 计算语言学 计算语言学
背景情况:
- 大型语言模型 (LLM) 代表了人工智能的重大进步.
- 目前正在讨论LLM对人类语言理解的更广泛研究的适用性.
- 现有的讨论往往侧重于LLM绩效指标,而不是潜在的机制.
研究的目的:
- 评估LLM作为人类语言理解模型的潜力.
- 将辩论的重点转移到对LLM内部工作的经验调查.
- 解决针对LLM作为人类语言模型可信性的常见论点.
主要方法:
- 分析当前的LLM能力和局限性.
- 对LLM表示和处理的实证研究的审查.
- 对有关符号结构和基础的批评提出反辩的发展.
主要成果:
- 在任务中LLM的表现不仅决定了它们作为人类语言模型的适用性.
- 经验趋势表明,关于LLM缺乏符号结构和基础的假设可能已经过时了.
- 关于LLM无法为人类语言表达和理解提供信息的最终结论还为时过早.
结论:
- 法学士对人类语言研究的相关性取决于他们的基本能力,而不仅仅是任务的执行.
- 对LLM表示和算法的经验性表征对于评估它们的认知合理性至关重要.
- 需要进一步的研究来探索LLM和人类语言处理之间的细微关系.
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