Summary of Cognidual Framework: Self-training Large Language Models Within a Dual-system Theoretical Framework For Improving Cognitive Tasks, by Yongxin Deng (1) et al.
CogniDual Framework: Self-Training Large Language Models within a Dual-System Theoretical Framework for Improving Cognitive Tasks
by Yongxin Deng, Xihe Qiu, Xiaoyu Tan, Chao Qu, Jing Pan, Yuan Cheng, Yinghui Xu, Wei Chu
First submitted to arxiv on: 5 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the potential for large language Models (LLMs) to emulate human cognition by introducing a novel framework called CogniDual Framework for LLMs (CFLLMs). This framework assesses whether LLMs can evolve from deliberate deduction to intuitive responses through self-training, mimicking the human process of acquiring new information. The study finds that the cognitive mechanisms behind LLMs’ response generation enhance our understanding of their capabilities in cognitive psychology, with practical implications for reducing computational demands during inference. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how big language models can think like humans do. It makes a special framework to see if these models can learn and get better at giving answers by themselves, just like we do when we first learn something new. The study shows that the way the models work is similar to how our brains work, which helps us understand them better. This could be useful because it means the models can give answers faster without using up too much computer power. |
Keywords
» Artificial intelligence » Inference » Self training
