arXiv:2510.04584v2 Announce Type: replace 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
The rapid advancement and adoption of large audio language models (LALMs) necessitate robust evaluation methods, revealing vulnerabilities in current assessment frameworks.
The identified instability in LALM evaluation metrics means reported performance may be artificially inflated or misleading, impacting R&D, deployment decisions, and public trust in AI capabilities.
The understanding of LALM robustness changes, highlighting the need for more sophisticated, context-aware evaluation benchmarks that move beyond simple multiple-choice accuracy.
- · AI evaluation framework developers
- · Third-party AI auditors
- · Researchers focused on model robustness
- · Developers relying solely on current MCQA metrics
- · Companies making deployment decisions based on superficial benchmarks
- · AI models that are less robust to subtle input variations
Further research and development will focus on creating more robust and reliable evaluation methodologies for LALMs.
AI developers will be incentivized to design models that are intrinsically more robust to evaluation nuances, potentially leading to more generalized and reliable AI.
Increased scrutiny on AI benchmark reporting could lead to industry-wide standards for model evaluation transparency and reproducibility, potentially slowing down perceived 'progress' in favor of solidity.
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Read at arXiv cs.CL