Evaluating Hallucinations in Multimodal LLMs with Spoken Queries under Diverse Acoustic Conditions
This addresses a critical challenge for voice-driven interfaces, highlighting an underexplored issue that impacts reliability in real-world applications.
The paper tackled the problem of hallucinations in multimodal large language models when using spoken queries, finding that error rates increased by 3% under clean speech and up to 20% with environmental noise compared to written queries.
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.