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Galatzer-Levy et al. (2024)

The Cognitive Capabilities of Generative AI: A Comparative Analysis with Human Benchmarks

arXiv

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Abstract

There is increasing interest in tracking the capabilities of general intelligence foundation models. This study benchmarks leading large language models and vision language models against human performance on the Wechsler Adult Intelligence Scale (WAIS-IV), a comprehensive, population-normed assessment of underlying human cognition and intellectual abilities, with a focus on the domains of VerbalComprehension (VCI), Working Memory (WMI), and Perceptual Reasoning (PRI). Most models demonstrated exceptional capabilities in the storage, retrieval, and manipulation of tokens such as arbitrary sequences of letters and numbers, with performance on the Working Memory Index (WMI) greater or equal to the 99.5th percentile when compared to human population normative ability. Performance on the Verbal Comprehension Index (VCI) which measures retrieval of acquired information, and linguistic understanding about the meaning of words and their relationships to each other, also demonstrated consistent performance at or above the 98th percentile. Despite these broad strengths, we observed consistently poor performance on the Perceptual Reasoning Index (PRI; range 0.1-10th percentile) from multimodal models indicating profound inability to interpret and reason on visual information. Smaller and older model versions consistently performed worse, indicating that training data, parameter count and advances in tuning are resulting in significant advances in cognitive ability.

Reference

Galatzer-Levy, I. R., Munday, D., McGiffin, J., Liu, X., Karmon, D., Labzovsky, I., Moroshko, R., Zait, A., & McDuff, D. (2024). The Cognitive Capabilities of Generative AI: A Comparative Analysis with Human Benchmarks. arXiv preprint arXiv:2410.07391. https://doi.org/10.48550/arxiv.2410.07391

What Rests on This

1 mechanism cites this source. Each finding below is quoted as it appears on that mechanism's own page.

Semantic Knowledge Memory Systems

  • Measured in AI

    A 2024 preprint gave 10 AI chat models the WAIS-IV Information subtest, general-knowledge questions such as "Who was the first president of the United States?", using the manual's instructions and items, with answers scored by clinical psychologists against norms for ages 25 to 29; 9 of the 10 models earned a scaled score of 19, the test's highest, at the 99.9th percentile

  • Measured in AI

    The spread across systems on the knowledge questions was narrow: all 10 models from OpenAI, Google and Anthropic scored 18 or 19, including the small on-device Gemini Nano, whereas word-meaning subtests varied far more (Vocabulary 4 to 19, Similarities 1 to 15)

  • Measured in AI

    The comparison is treated as direct because the questions, scoring and norms were the human ones and the answer was not in view for either side, but items were typed rather than spoken, it is a single preprint, and the authors report no check on whether the test items appeared in the models' training text (what the model learned from before release)