  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.
