LVLMs and Humans Ground Differently in Referential Communication
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Computer Science > Computation and Language
Title:LVLMs and Humans Ground Differently in Referential Communication
Abstract:For generative AI agents to partner effectively with human users, the ability to accurately predict human intent is critical. But this ability to collaborate remains limited by a critical deficit: an inability to model common ground. We present a referential communication experiment with a factorial design involving director-matcher pairs (human-human, human-AI, AI-human, and AI-AI) that interact with multiple turns in repeated rounds to match pictures of objects not associated with any obvious lexicalized labels. We show that LVLMs cannot interactively generate and resolve referring expressions in a way that enables smooth communication, a crucial skill that underlies human language use. We release our corpus of 356 dialogues (89 pairs over 4 rounds each) along with the online pipeline for data collection and the tools for analyzing accuracy, efficiency, and lexical overlap.
| Comments: | 27 pages, 16 figures |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC) |
| Cite as: | arXiv:2601.19792 [cs.CL] |
| (or arXiv:2601.19792v4 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2601.19792
arXiv-issued DOI via DataCite
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Submission history
From: Peter Zeng [view email][v1] Tue, 27 Jan 2026 16:52:20 UTC (7,009 KB)
[v2] Wed, 28 Jan 2026 14:28:33 UTC (7,009 KB)
[v3] Mon, 20 Apr 2026 15:09:57 UTC (7,236 KB)
[v4] Mon, 15 Jun 2026 23:55:44 UTC (7,236 KB)
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