Integration of cognitive tasks into artificial general intelligence test for large models
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910354702860288 |
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| author | Qu, Youzhi Wei, Chen Du, Penghui Che, Wenxin Zhang, Chi Ouyang, Wanli Bian, Yatao Xu, Feiyang Hu, Bin Du, Kai Wu, Haiyan Liu, Jia Liu, Quanying |
| author_facet | Qu, Youzhi Wei, Chen Du, Penghui Che, Wenxin Zhang, Chi Ouyang, Wanli Bian, Yatao Xu, Feiyang Hu, Bin Du, Kai Wu, Haiyan Liu, Jia Liu, Quanying |
| contents | During the evolution of large models, performance evaluation is necessarily performed to assess their capabilities and ensure safety before practical application. However, current model evaluations mainly rely on specific tasks and datasets, lacking a united framework for assessing the multidimensional intelligence of large models. In this perspective, we advocate for a comprehensive framework of cognitive science-inspired artificial general intelligence (AGI) tests, aimed at fulfilling the testing needs of large models with enhanced capabilities. The cognitive science-inspired AGI tests encompass the full spectrum of intelligence facets, including crystallized intelligence, fluid intelligence, social intelligence, and embodied intelligence. To assess the multidimensional intelligence of large models, the AGI tests consist of a battery of well-designed cognitive tests adopted from human intelligence tests, and then naturally encapsulates into an immersive virtual community. We propose increasing the complexity of AGI testing tasks commensurate with advancements in large models and emphasizing the necessity for the interpretation of test results to avoid false negatives and false positives. We believe that cognitive science-inspired AGI tests will effectively guide the targeted improvement of large models in specific dimensions of intelligence and accelerate the integration of large models into human society. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_02547 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Integration of cognitive tasks into artificial general intelligence test for large models Qu, Youzhi Wei, Chen Du, Penghui Che, Wenxin Zhang, Chi Ouyang, Wanli Bian, Yatao Xu, Feiyang Hu, Bin Du, Kai Wu, Haiyan Liu, Jia Liu, Quanying Artificial Intelligence Computation and Language During the evolution of large models, performance evaluation is necessarily performed to assess their capabilities and ensure safety before practical application. However, current model evaluations mainly rely on specific tasks and datasets, lacking a united framework for assessing the multidimensional intelligence of large models. In this perspective, we advocate for a comprehensive framework of cognitive science-inspired artificial general intelligence (AGI) tests, aimed at fulfilling the testing needs of large models with enhanced capabilities. The cognitive science-inspired AGI tests encompass the full spectrum of intelligence facets, including crystallized intelligence, fluid intelligence, social intelligence, and embodied intelligence. To assess the multidimensional intelligence of large models, the AGI tests consist of a battery of well-designed cognitive tests adopted from human intelligence tests, and then naturally encapsulates into an immersive virtual community. We propose increasing the complexity of AGI testing tasks commensurate with advancements in large models and emphasizing the necessity for the interpretation of test results to avoid false negatives and false positives. We believe that cognitive science-inspired AGI tests will effectively guide the targeted improvement of large models in specific dimensions of intelligence and accelerate the integration of large models into human society. |
| title | Integration of cognitive tasks into artificial general intelligence test for large models |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2402.02547 |