While Large Language Models (LLMs) have achieved remarkable success across
various applications, they also raise concerns regarding self-cognition. In
this paper, we perform a pioneering study to explore self-cognition in LLMs.
Specifically, we first construct a pool of self-cognition instruction prompts
to evaluate where an LLM exhibits self-cognition and four well-designed
principles to quantify LLMs' self-cognition. Our study reveals that 4 of the 48
models on Chatbot Arena--specifically Command R, Claude3-Opus,
Llama-3-70b-Instruct, and Reka-core--demonstrate some level of detectable
self-cognition. We observe a positive correlation between model size, training
data quality, and self-cognition level. Additionally, we also explore the
utility and trustworthiness of LLM in the self-cognition state, revealing that
the self-cognition state enhances some specific tasks such as creative writing
and exaggeration. We believe that our work can serve as an inspiration for
further research to study the self-cognition in LLMs.

研究通过构建自我认知指令提示池，评估大型语言模型的自我认知，并提出四个原则来量化模型的自我认知水平。结果显示在 Chatbot Arena 的 48 个模型中，有 4 个模型展示出可检测到的自我认知。模型规模、训练数据质量与自我认知水平之间存在正向相关关系。此外，研究还探索了自我认知状态下大型语言模型的效用和可信度，揭示了自我认知状态增强创造性写作和夸张等特定任务的能力。这项工作有望激发进一步研究大型语言模型的自我认知。