CogniDual Framework: Self-Training Large Language Models within a Dual-System Theoretical Framework for Improving Cognitive Tasks
<|reference_start|>CogniDual Framework: Self-Training Large Language Models within a Dual-System Theoretical Framework for Improving Cognitive Tasks: Cognitive psychology investigates perception, attention, memory, language, problem-solving, decision-making, and reasoning. Kahneman's dual-system theory elucidates the human decision-making process, distinguishing between the rapid, intuitive System 1 and the deliberative, rational System 2. Recent advancements have positioned large language Models (LLMs) as formidable tools nearing human-level proficiency in various cognitive tasks. Nonetheless, the presence of a dual-system framework analogous to human cognition in LLMs remains unexplored. This study introduces the \textbf{CogniDual Framework for LLMs} (CFLLMs), designed to assess whether LLMs can, through self-training, evolve from deliberate deduction to intuitive responses, thereby emulating the human process of acquiring and mastering new information. Our findings reveal the cognitive mechanisms behind LLMs' response generation, enhancing our understanding of their capabilities in cognitive psychology. Practically, self-trained models can provide faster responses to certain queries, reducing computational demands during inference.<|reference_end|>
@article{deng2024cognidual,
    title={CogniDual Framework: Self-Training Large Language Models within a
  Dual-System Theoretical Framework for Improving Cognitive Tasks},
    author={Yongxin Deng (1), Xihe Qiu (1), Xiaoyu Tan (2), Chao Qu (2), Jing Pan
  (3), Yuan Cheng (2), Yinghui Xu (4), Wei Chu (2) ((1) School of Electronic
  and Electrical Engineering, Shanghai University of Engineering Science,
  Shanghai, China, (2) INF Technology (Shanghai) Co., Ltd., Shanghai, China,
  (3) School of Art, Design and Architecture, Monash University, Melbourne,
  Australia, (4) Artificial Intelligence Innovation and Incubation Institute,
  Fudan University, Shanghai, China)},
    journal={arXiv preprint arXiv:2409.03381},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.03381},
    primaryClass={cs.CL cs.AI}
}