Authors: 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

Published on: February 04, 2024

Impact Score: 8.45

Arxiv code: Arxiv:2402.02547

Summary

• What is new: A comprehensive AGI test framework that bridges cognitive science and natural language processing to assess multidimensional intelligence of large models.
• Why this is important: Lack of a unified framework for evaluating the multidimensional intelligence of large models.
• What the research proposes: An AGI test framework incorporating elements of cognitive science for evaluating crystallized, fluid, social, and embodied intelligence in large models.
• Results: The proposal of increasing complexity in AGI testing tasks aligned with advancements in large models and the importance of accurate interpretation of test results.

Technical Details

Technological frameworks used: AGI test framework bridging cognitive science and natural language processing

Models used: Large language models and multi-modal large models

Data used: A battery of cognitive tests adopted from human intelligence tests

Potential Impact

AI and machine learning development companies, cognitive computing sectors, educational technology firms, and businesses integrating AI for enhanced decision-making and automation

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