Evaluating Hallucinations in Multimodal LLMs with Spoken Queries under Diverse Acoustic Conditions

• URL: http://arxiv.org/abs/2510.08581v1
• Date: Fri, 19 Sep 2025 07:18:45 GMT
• Title: Evaluating Hallucinations in Multimodal LLMs with Spoken Queries under Diverse Acoustic Conditions
• Authors: Hansol Park, Hoseong Ahn, Junwon Moon, Yejin Lee, Kyuhong Shim,
• Abstract summary: We investigate how spoken input influences hallucinations in large language models.<n>We present RePOPE-Spk, an audio-augmented extension of the RePOPE benchmark, where queries are provided as speech under diverse acoustic conditions.<n> Experimental results show that hallucinations escalate when queries are spoken rather than written.
• Score: 10.361060366260729
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Hallucinations in vision-language models have been extensively studied using benchmarks that probe reliability in image-text settings. In contrast, the effect of spoken queries on multimodal hallucinations remains largely unexplored, despite the growing role of voice-driven interfaces. In this work, we investigate how spoken input influences hallucinations in multimodal large language models. We present RePOPE-Spk, an audio-augmented extension of the RePOPE benchmark, where queries are provided as speech under diverse acoustic conditions. Using RePOPE-Spk, we systematically evaluate both proprietary and open-source models. Experimental results show that hallucinations escalate when queries are spoken rather than written: error rates increase by 3% under clean speech and by up to 20% with environmental noise. Input order and query length further affect robustness, while strategies such as many-shot prompting and chain-of-thought reasoning offer partial but insufficient mitigation. These findings highlight a critical and underexplored challenge, opening new directions for building reliable voice interface systems.

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