Multimodal Large Language Models as Synthetic Participants in Video-Based Studies: An Evaluation
Multimodal large language models (MLLMs) have shown strong performance on objective tasks such as video understanding and reasoning. However, it remains unclear whether they can approximate subjective human responses, which depend not only on content comprehension but also on individuals' social contexts. To address this gap, we evaluate MLLMs as synthetic participants in an emerging task: assessing perceived sensory engagement with short videos. Grounded in the Perceived Message Sensation Value (PMSV) framework, we compare ratings from recruited human participants and profile-conditioned MLLM simulations (n=673) using a 17-item scale measuring emotional arousal, dramatic impact, and nove...
| Authors | Prabal Shrestha, Bohan Jiang, Haoning Xue et al. |
|---|---|
| Primary category | cs.HC |
| Published | 1 May 2026 |
| Last updated at source | 1 May 2026 |
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