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CogGPT: Unleashing the Power of Cognitive Dynamics on Large Language Models

Yaojia Lv, Haojie Pan, Zekun Wang, Jiafeng Liang, Yuanxing Liu, Ruiji Fu, Ming Liu, Zhongyuan Wang, Bing Qin


Abstract
Cognitive dynamics, which refer to the evolution in human cognitive processes, are pivotal to advance human understanding of the world. Recent advancements in large language models (LLMs) highlight their potential for cognitive simulation. However, these LLM-based cognitive studies primarily focus on replicating human cognition in specific contexts, overlooking the inherently dynamic nature of cognition. To bridge this gap, we explore the cognitive dynamics of LLMs and present a corresponding task inspired by longitudinal studies. Toward 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 further introduce CogGPT for the task, which features an innovative iterative cognitive mechanism to develop lifelong cognitive dynamics. Empirical results demonstrate the superiority of CogGPT over several existing methods, particularly in its ability to facilitate role-specific cognitive dynamics under continuous information flows. We will release the code and data to enable further research.
Anthology ID:
2024.findings-emnlp.352
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2024
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6074–6091
Language:
URL:
https://aclanthology.org/2024.findings-emnlp.352
DOI:
10.18653/v1/2024.findings-emnlp.352
Bibkey:
Cite (ACL):
Yaojia Lv, Haojie Pan, Zekun Wang, Jiafeng Liang, Yuanxing Liu, Ruiji Fu, Ming Liu, Zhongyuan Wang, and Bing Qin. 2024. CogGPT: Unleashing the Power of Cognitive Dynamics on Large Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 6074–6091, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
CogGPT: Unleashing the Power of Cognitive Dynamics on Large Language Models (Lv et al., Findings 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.findings-emnlp.352.pdf
Software:
 2024.findings-emnlp.352.software.zip
Data:
 2024.findings-emnlp.352.data.zip