Evaluating Multimodal LLMs across Text and Audio Modalities for Accessible Disaster Assistance

[2608.14651] Evaluating Multimodal LLMs across Text and Audio Modalities for Accessible Disaster Assistance Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Artificial Intelligence arXiv:2608.14651 (cs) [Submitted on 30 Jul 2026] Title: Evaluating Multimodal LLMs across Text and Audio Modalities for Accessible Disaster Assistance Authors: Anuridhi Gupta , Samara Mansoor , Hemant Purohit View a PDF of the paper titled Evaluating Multimodal LLMs across Text and Audio Modalities for Accessible Disaster Assistance, by Anuridhi Gupta and 2 other authors View PDF HTML (experimental) Abstract: Effective disaster risk communication is a foundational humanitarian challenge, yet current emergency infrastructure fails to meet the needs of individuals with access and functional needs, including hard-of-hearing individuals, pregnant women, mothers with toddlers, and elderly individuals with dementia. Recent advancements in Artificial Intelligence (AI), especially Multi-Modal Large Language Models (MM-LLMs), demonstrate powerful capabilities to serve diverse users across text, audio, image, and video modalities within a single unified system, such as a chatbot. However, their suitability for deployment rests on a property that receives limited scrutiny, i.e., whether these systems produce consistent, actionable outputs regardless of the modality through which a user communicates. In this paper, we conduct a comprehensive analysis to understand the status of open-weight MM-LLMs using real emergency alert scenarios across four different vulnerable personas. These state-of-the-art (SOTA) models are evaluated on consistency of responses across text and audio modalities when the same task scenario is given. Findings indicate that no model achieves reliable consistency across modalities, and that performance gaps are heightened for personas with access needs, introducing modality-dependent inequity that undermines the humanitarian value of these systems. These results inform concrete design recommendations for building equitable, trustworthy, and inclusive AI tools for disaster risk communication. Comments: 8 pages, 3 figures Subjects: Artificial Intelligence (cs.AI) ; Human-Computer Interaction (cs.HC) ACM classes: H.5.2; I.2.7; K.4.2 Cite as: arXiv:2608.14651 [cs.AI] (or arXiv:2608.14651v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.14651 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Anuridhi Gupta [ view email ] [v1] Thu, 30 Jul 2026 20:09:09 UTC (1,196 KB) Full-text links: Access Paper: View a PDF of the paper titled Evaluating Multimodal LLMs across Text and Audio Modalities for Accessible Disaster Assistance, by Anuridhi Gupta and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2026-08 Change to browse by: cs cs.HC References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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