Kavli Affiliate: Jia Liu
| First 5 Authors: Youzhi Qu, Chen Wei, Penghui Du, Wenxin Che, Chi Zhang
| Summary:
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.
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