@inproceedings{li-etal-2026-awarenessbench,
title = "{A}wareness{B}ench: Assessing Cognitive Capabilities of Language Models",
author = "Li, Xiaojian and
Xu, Rongwu and
Zhang, Tianyun and
Wang, Yue and
Chen, Shuo and
Lyu, Qiner and
Zhang, Briana and
Yang, Peiran and
Chen, Kyle Xue and
Shi, Haoyuan and
Wang, Yu and
Xu, Wei",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.124/",
doi = "10.18653/v1/2026.acl-long.124",
pages = "2682--2741",
ISBN = "979-8-89176-390-6",
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."
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<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>
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%0 Conference Proceedings
%T AwarenessBench: Assessing Cognitive Capabilities of Language Models
%A Li, Xiaojian
%A Xu, Rongwu
%A Zhang, Tianyun
%A Wang, Yue
%A Chen, Shuo
%A Lyu, Qiner
%A Zhang, Briana
%A Yang, Peiran
%A Chen, Kyle Xue
%A Shi, Haoyuan
%A Wang, Yu
%A Xu, Wei
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F li-etal-2026-awarenessbench
%X 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.
%R 10.18653/v1/2026.acl-long.124
%U https://aclanthology.org/2026.acl-long.124/
%U https://doi.org/10.18653/v1/2026.acl-long.124
%P 2682-2741
Markdown (Informal)
[AwarenessBench: Assessing Cognitive Capabilities of Language Models](https://aclanthology.org/2026.acl-long.124/) (Li et al., ACL 2026)
ACL
- Xiaojian Li, Rongwu Xu, Tianyun Zhang, Yue Wang, Shuo Chen, Qiner Lyu, Briana Zhang, Peiran Yang, Kyle Xue Chen, Haoyuan Shi, Yu Wang, and Wei Xu. 2026. AwarenessBench: Assessing Cognitive Capabilities of Language Models. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2682–2741, San Diego, California, United States. Association for Computational Linguistics.