Introduction
Large Language Models (LLMs) have recently achieved impressive performance on a wide variety of Natural Language Processing (NLP) tasks, including text comprehension (Kenton and Toutanova, 2019 ) , reasoning (Talmor et al., 2020 ; Webb et al., 2023 ) , code generation (Chen et al., 2021 ) , and mathematical problems (Fu et al., 2023 ) .
However, few studies have explored the reasons behind the evolutionary relationship among various abilities, which makes it difficult for to understand the development of LLMs’ capabilities as a whole and may pose potential risks to their further development.

To this end, we introduce Piaget’s Theory of Cognitive Development (PTC) (Piaget et al., 1952 ; Flavell, 1977 ; Badakar et al., 2017 ) , which posits that the development of cognitive levels forms the foundation for human learning across various abilities.
Inspired by this, we think that studying the cognitive development of LLMs can assist us in better understanding the current performance of LLMs on downstream tasks and illuminate the path for future enhancements of their capabilities.
As the most authoritative theory in the development of psychology, PTC suggests that human children move through four different stages of learning, including the sensorimotor stage (0-2 years old), the preoperational stage (2-7 years old), the concrete operational stage (7-12 years old), and the formal operational stage (above 12 years old). Children in different cognitive stages exhibit significantly distinct patterns of thinking and cognitive abilities, which affects their learning of other skills.
Examining LLMs from the perspective of PTC, some natural and crucial questions are: At what stage has the cognitive ability of LLMs developed compared to humans at present? What are the key factors that affect the cognitive abilities of LLMs? Is the emergence of advanced abilities and performance bottlenecks in current LLMs related to their cognitive levels?

To explore the above questions, we construct a benchmark based on the scenario experiments used in PTC for evaluating the cognitive abilities of LLMs, denoted as CogLM . A large-scale human trial was conducted involving 207 participants aged between 6 and 20 years to ensure the alignment between the CogLM and PTC.
We then perform extensive experiments on CogLM over several series of language models, including OPT (Zhang et al., 2022 ) , Llama-2 (Touvron et al., 2023 ) , GPT-3.5-Turbo and GPT-4 (OpenAI, 2023 ) . Our results indicate that: (1) Advanced LLMs, such as GPT-4, have developed human-like cognitive abilities, matching those of a 20-year-old individual. (2) Two primary factors influencing these cognitive capacities in LLMs are the size of their parameters and their optimization objectives. (3) There is a positive correlation between the cognitive level of LLMs and their performance in downstream tasks.

We believe that our findings can present a clear understanding of the current cognitive level of LLMs and provide insights into the emergence of advanced abilities in LLMs, shedding light on the future development of them. Our contributions can be summarised as follows:

We innovatively introduce Piaget’s Theory of Cognitive Development (PTC) to analyze the development of cognitive abilities of LLMs.

We construct a high-quality benchmark ( CogLM ) for evaluating the cognitive ability level of LLMs.

Comprehensive experiments across multiple LLM series on CogLM demonstrate the cognitive level of current LLMs, key factors responsible for their varying levels, and relationships between cognitive levels and performance on downstream tasks.