A failure of Imagination: VLMs fail to use visual representations for text, but self-distillation can help
Vision-language (VL) models are typically built by fine-tuning a language model to attend to visual features in service of a Visual Question-Answering objective. We ask whether this alignment changes how language models represent text---especially on tasks where visualization would help, like geometry questions posed in words. We distinguish weak multimodality (the model can answer questions about an input image) from strong multimodality (the model's text representations themselves become vision-aligned, improving performance on text-only tasks with latent visual structure). To test for strong multimodality, we construct a task of shape-counting problems with paired equivalent text and i...
| Authors | Shashwat Singh, He He, Tal Linzen |
|---|---|
| Primary category | cs.AI |
| Published | 18 October 2026 |
| Last updated at source | 18 October 2026 |
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