对比大模型与人类在语言和现实推理中的表征差异。
LLM and Human Modes of Representation
- 分析大模型与人类在语言知识表征上的异同
- 大模型语言表现流畅但学习效率低于人类
- 适合关注认知机制与AI对比的研究者
关于人工智能认知基础的研究，常聚焦于大型语言模型（LLMs）与人类在信息处理与表征方式上的比较。一方面考察大模型在多种认知任务上能否达到甚至超越人类表现；另一方面探索两者在信息处理中的共性与差异。本文探讨近期在两个信息领域中的相关研究：一是语言知识的表征，二是现实世界推理与规划。尽管大模型在语言应用中常表现出色且流利，但其对语言内容的处理方式与人类存在显著差异。此外，在推理任务的学习与泛化方面，大模型整体上仍不如人类高效。
原文摘要 · Abstract (English)
Much work on the cognitive foundations of AI has focussed on comparisons between the ways in which Large Language Models (LLMs) and humans process information and represent it. One aspect of this comparison involves determining the extent to which LLMs can achieve or surpass human performance on a variety of cognitively interesting tasks. A second explores points of convergence and divergence between LLM and human systems for processing information. Here, I consider some recent research that has addressed both issues in two informational domains. The first is the representation of linguistic knowledge. The second is real world reasoning and planning. While LLMs frequently achieve impressive levels of performance and fluency on linguistic applications, they tend to handle linguistic content in ways that are distinct from human processing. They are also, for the most part, less efficient than humans in learning and generalisation for reasoning tasks.
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