Where did the ambiguity go? Examining how multimodal models interpret polysemous words
Human language is highly polysemous. Many common words (e.g., "bank" or "palm") carry several distinct meanings that shape what humans communicate and imagine. Large language models (LLMs) have been shown to understand this multiplicity of meaning, but much less is known about how polysemy surfaces in other modalities such as images. We study this across 17 text-to-image and 15 text-generation models by giving each a polysemous word with no context to fix its meaning and measuring which senses are produced over many samples. We find a clear multimodal gap, where within every model family, generated images settle on far fewer senses than generated sentences (normalized entropy 0.10 vs. 0.2...
| Authors | Jasin Cekinmez, Addison J. Wu, Raja Marjieh et al. |
|---|---|
| Primary category | cs.AI |
| Published | 1 August 2026 |
| Last updated at source | 16 August 2026 |
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