Robustness assessment of large audio language models in multiple-choice evaluation
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Computer Science > Computation and Language
Title:Robustness assessment of large audio language models in multiple-choice evaluation
Abstract:Recent advances in large audio language models (LALMs) have primarily been assessed using a multiple-choice question answering (MCQA) framework. However, subtle changes, such as shifting the order of choices, result in substantially different results. Existing MCQA frameworks do not account for this variability and report a single accuracy number per benchmark or category. We dive into the MCQA evaluation framework and conduct a systematic study spanning three benchmarks (MMAU, MMAR and MMSU) and four models: Audio Flamingo 2, Audio Flamingo 3, Qwen2.5-Omni-7B-Instruct, and Kimi-Audio-7B-Instruct. Our findings indicate that models are sensitive not only to the ordering of choices, but also to the paraphrasing of the question and the choices. Finally, we propose a simpler evaluation protocol and metric that account for subtle variations and provide a more detailed evaluation report of LALMs within the MCQA framework.
| Comments: | Accepted in Interspeech 2026 |
| Subjects: | Computation and Language (cs.CL); Sound (cs.SD); Audio and Speech Processing (eess.AS) |
| Cite as: | arXiv:2510.04584 [cs.CL] |
| (or arXiv:2510.04584v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2510.04584
arXiv-issued DOI via DataCite
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Submission history
From: Fernando López PhD(c) [view email][v1] Mon, 6 Oct 2025 08:36:17 UTC (99 KB)
[v2] Wed, 24 Jun 2026 15:08:01 UTC (93 KB)
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