Evidence of interrelated cognitive-like capabilities in large language models: Indications of artificial general intelligence or achievement?

Understand

Large language models (LLMs) are advanced artificial intelligence (AI) systems that can perform a variety of tasks commonly found in human intelligence tests, such as defining words, performing calculations, and engaging in verbal reasoning.

• There are also substantial individual differences in LLM capacities.
• Given the consistent observation of a positive manifold and general intelligence factor in human samples, along with group-level factors (e.g., crystallized intelligence), we hypothesized that LLM test scores may also exhibit positive intercorrelations, which could potentially give rise to an artificial general ability (AGA) factor and one or more group-level factors.
• Based on a sample of 591 LLMs and scores from 12 tests aligned with fluid reasoning (Gf), domain-specific knowledge (Gkn), reading/writing (Grw), and quantitative knowledge (Gq), we found strong empirical evidence for a positive manifold and a general factor of ability.