Hallucinations in multimodal models have been extensively studied using benchmarks that probe reliability in image-text query settings. However, the effect of spoken queries on multimodal hallucinations remains largely unexplored, despite the growing role of voice interfaces. In this paper, we introduce a systematic pipeline that converts existing multimodal hallucination benchmarks into spoken-query versions while preserving the original tasks and labels. We instantiate this pipeline on RePOPE and release RePOPE-Spk, where all queries are provided as spoken audio under diverse input conditions. Experimental results show that hallucinations escalate when queries are spoken rather than written: error rates increase by 3-6% with clean speech and by up to 30% under environmental noise. Furthermore, many-shot prompting and chain-of-thought reasoning provide only partial mitigation. Our findings motivate new directions for building reliable voice interface systems and evaluations.
@article{arxiv.2510.08581,
title = {Evaluating Hallucinations in Audio-Visual Multimodal LLMs with Spoken Queries under Diverse Acoustic Conditions},
author = {Hansol Park and Hoseong Ahn and Junwon Moon and Yejin Lee and Kyuhong Shim},
journal= {arXiv preprint arXiv:2510.08581},
year = {2026}
}