Summary of Integration Of Cognitive Tasks Into Artificial General Intelligence Test For Large Models, by Youzhi Qu et al.
Integration of cognitive tasks into artificial general intelligence test for large models
by Youzhi Qu, Chen Wei, Penghui Du, Wenxin Che, Chi Zhang, Wanli Ouyang, Yatao Bian, Feiyang Xu, Bin Hu, Kai Du, Haiyan Wu, Jia Liu, Quanying Liu
First submitted to arxiv on: 4 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 proposes a comprehensive framework for evaluating the multidimensional intelligence of large language models, inspired by cognitive science and artificial general intelligence (AGI) concepts. The framework aims to assess various facets of intelligence, including crystallized, fluid, social, and embodied intelligence, using a battery of cognitive tests adapted from human intelligence assessments. This approach can help guide targeted improvements in specific dimensions of intelligence and accelerate the integration of large models into society. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper suggests a new way to test big language models to see how smart they are. Right now, we mainly use special tasks and datasets to evaluate their performance, but this isn’t enough. We need a framework that looks at all aspects of intelligence, like problem-solving skills, social understanding, and creativity. The proposed approach uses tests inspired by human intelligence assessments and creates an immersive virtual community to assess the models’ abilities. This can help us make better large models and integrate them into our daily lives. |
