Piaget's Theory of Cognitive Development (PTC) posits that the development of cognitive levels forms the foundation for human learning across various abilities. As Large Language Models (LLMs) have recently shown remarkable abilities across a wide variety of tasks, we are curious about the cognitive levels of current LLMs: to what extent they have developed and how this development has been achieved. To this end, we construct a benchmark CogLM (Cognitive Ability Evaluation for Language Model) based on PTC to assess the cognitive levels of LLMs. CogLM comprises 1,220 questions spanning 10 cognitive abilities crafted by more than 20 human experts, providing a comprehensive testbed for the cognitive levels of LLMs. Through extensive experiments across multiple mainstream LLMs with CogLM, we find that: (1) Human-like cognitive abilities have emerged in advanced LLMs (GPT-4), comparable to those of a 20-year-old human. (2) The parameter size and optimization objective are two key factors affecting the cognitive levels of LLMs. (3) The performance on downstream tasks is positively correlated with the level of cognitive abilities. These findings fill the gap in research on the cognitive abilities of LLMs, tracing the development of LLMs from a cognitive perspective and guiding the future direction of their evolution.

本研究针对大型语言模型（LLMs）在认知能力上的发展进行评估，填补了相关研究的空白。通过构建基于皮亚杰认知发展理论的CogLM基准，研究发现高级LLMs（如GPT-4）展现出类似20岁人类的认知能力，并指出参数规模和优化目标是影响LMMs认知水平的关键因素。这些发现有助于指导LLMs未来的演进方向。

CogLM：大型语言模型的认知发展跟踪