Humans vs Vision-Language Models: A Unified Measure of Narrative Coherence
We study narrative coherence in visually grounded stories by comparing human-written narratives with those generated by vision-language models (VLMs) on the Visual Writing Prompts corpus. Using a set of metrics that capture different aspects of narrative coherence, including coreference, discourse relation types, topic continuity, character persistence, and multimodal character grounding, we compute a narrative coherence score. We find that VLMs show broadly similar coherence profiles that differ systematically from those of humans. In addition, differences for individual measures are often subtle, but they become clearer when considered jointly. Overall, our results indicate that, despit...
| Authors | Nikolai Ilinykh, Hyewon Jang, Shalom Lappin et al. |
|---|---|
| Primary category | cs.CL |
| Published | 26 March 2026 |
| Last updated at source | 26 March 2026 |
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