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This study introduces AwarenessBench, the first comprehensive benchmark designed to evaluate the cognitive capabilities of large language models across metacognition, self-awareness, social awareness, and situational awareness. The research demonstrates that while advanced models can surpass human averages in general tasks, they still significantly lag behind humans in higher-order cognitive functions like metacognition, proving that improvements in reasoning do not automatically translate to genuine awareness.
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AwarenessBench: Assessing Cognitive Capabilities of Language Models

arXiv2 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2609.35409v1 Announce Type: new Abstract: As language models (LMs) exhibit increasingly consciousness-like behaviors, evaluating their cognitive abilities becomes essential. We introduce AwarenessBench, the first comprehensive benchmark for assessing the cognitive abilities of LMs in four dimensions: metacognition, self-awareness, social awareness, and situational awareness, covering 15 cognitive functions and 14,381 samples. Evaluating 18 state-of-the-art LMs, we find that all consistently surpass random baselines, with more advanced models performing better. We further compare LMs with human performance across three demographic groups, where the best-performing model surpasses human averages overall, but most still fall markedly short in metacognition and self-awareness. Finally, we show that awareness is a distinct capability: progress in language modeling or reasoning does not necessarily translate into improved cognition.

Abstract:As language models (LMs) exhibit increasingly consciousness-like behaviors, evaluating their cognitive abilities becomes essential. We introduce AwarenessBench, the first comprehensive benchmark for assessing the cognitive abilities of LMs in four dimensions: metacognition, self-awareness, social awareness, and situational awareness, covering 15 cognitive functions and 14,381 samples. Evaluating 18 state-of-the-art LMs, we find that all consistently surpass random baselines, with more advanced models performing better. We further compare LMs with human performance across three demographic groups, where the best-performing model surpasses human averages overall, but most still fall markedly short in metacognition and self-awareness. Finally, we show that awareness is a distinct capability: progress in language modeling or reasoning does not necessarily translate into improved cognition.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2609.35409 [cs.CL]

(or arXiv:2609.35409v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.35409 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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