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    <abstract>Multimodal language models (MLMs) increasingly demonstrate human-like communication, yet their use of everyday perspectival words remains poorly understood. To address this gap, we compare humans and MLMs in their use of three word types, which we predict impose increasing cognitive demands: vocabulary (e.g., ‘boat’ or ‘cup’), possessives (e.g., ‘mine’ vs. ‘yours’), and demonstratives (e.g., ‘this one’ vs. ‘that one’). Testing seven MLMs against human participants, we find that perspectival words are harder than vocabulary words for both groups. The gap is even larger for MLMs: while models approach human-level performance on using vocabulary, they exhibit clear deficits with possessives and even greater difficulties with demonstratives. Ablation analyses point to limitations in perspective-taking and spatial reasoning as key sources of these gaps in MLMs. Instruction-based prompting helps close the gap for possessives but still leaves demonstratives far below human performance. These results show that, unlike vocabulary, perspectival words pose a greater challenge in human communication—and this difficulty is further amplified in MLMs, revealing a crucial shortfall in their pragmatic and social-cognitive abilities.</abstract>
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%0 Conference Proceedings
%T Using Perspectival Words Is Harder Than Vocabulary Words for Humans —and Even More So for Multimodal Language Models
%A Dong, Dota Tianai
%A Luo, Yifan
%A Wang, Po-Ya Angela
%A Ozyurek, Asli
%A Rubio-Fernandez, Paula
%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 dong-etal-2026-using
%X Multimodal language models (MLMs) increasingly demonstrate human-like communication, yet their use of everyday perspectival words remains poorly understood. To address this gap, we compare humans and MLMs in their use of three word types, which we predict impose increasing cognitive demands: vocabulary (e.g., ‘boat’ or ‘cup’), possessives (e.g., ‘mine’ vs. ‘yours’), and demonstratives (e.g., ‘this one’ vs. ‘that one’). Testing seven MLMs against human participants, we find that perspectival words are harder than vocabulary words for both groups. The gap is even larger for MLMs: while models approach human-level performance on using vocabulary, they exhibit clear deficits with possessives and even greater difficulties with demonstratives. Ablation analyses point to limitations in perspective-taking and spatial reasoning as key sources of these gaps in MLMs. Instruction-based prompting helps close the gap for possessives but still leaves demonstratives far below human performance. These results show that, unlike vocabulary, perspectival words pose a greater challenge in human communication—and this difficulty is further amplified in MLMs, revealing a crucial shortfall in their pragmatic and social-cognitive abilities.
%U https://aclanthology.org/2026.acl-long.84/
%P 1845-1870

Markdown (Informal)

[Using Perspectival Words Is Harder Than Vocabulary Words for Humans —and Even More So for Multimodal Language Models](https://aclanthology.org/2026.acl-long.84/) (Dong et al., ACL 2026)

• Using Perspectival Words Is Harder Than Vocabulary Words for Humans —and Even More So for Multimodal Language Models (Dong et al., ACL 2026)

ACL

• Dota Tianai Dong, Yifan Luo, Po-Ya Angela Wang, Asli Ozyurek, and Paula Rubio-Fernandez. 2026. Using Perspectival Words Is Harder Than Vocabulary Words for Humans —and Even More So for Multimodal Language Models. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1845–1870, San Diego, California, United States. Association for Computational Linguistics.