CogGPT: Unleashing the Power of Cognitive Dynamics on Large Language
  Models
Yaojia Lv Haojie Pan Ruiji Fu Ming Liu Zhongyuan Wang Bing Qin
Cognitive dynamics are pivotal to advance human understanding of the world.
Recent advancements in large language models (LLMs) reveal their potential for
cognitive simulation. However, these LLM-based cognitive studies primarily
focus on static modeling, overlooking the dynamic nature of cognition. To
bridge this gap, we propose the concept of the cognitive dynamics of LLMs and
present a corresponding task with the inspiration of longitudinal studies.
Towards the task, we develop CogBench, a novel benchmark to assess the
cognitive dynamics of LLMs and validate it through participant surveys. We also
design two evaluation metrics for CogBench, including Authenticity and
Rationality. Recognizing the inherent static nature of LLMs, we introduce
CogGPT for the task, which features an innovative iterative cognitive mechanism
aimed at enhancing lifelong cognitive dynamics. Empirical results demonstrate
the superiority of CogGPT over existing methods, particularly in its ability to
facilitate role-specific cognitive dynamics under continuous information flows.