aiqbench analysis
This study critically examines whether post-training in large audio-language models (LALMs) yields genuine improvements in audio reasoning or merely induces adaptation to specific multiple-choice formats and textual cues. By disentangling these factors, the research reveals that many reported performance gains are artifacts of question format exploitation rather than robust advancements in understanding audio content. These findings are vital for researchers aiming to build trustworthy multimodal systems that generalize beyond narrow benchmark tricks.
What Does LALM Post-training Learn? Rethinking Audio Understanding and Reasoning Capabilities
arXiv:2610.19476v1 Announce Type: cross Abstract: Post-training often improves the performance of large audio-language models on audio understanding and reasoning benchmarks. However, these improvements may reflect adaptation to specific question formats, such as multiple-choice questions, rather than genuine advances in audio understanding and reasoning capabilities. To investigate what LALMs actually learn from post-training, we conduct controlled experiments that disentangle improvements in audio understanding from adaptation to specific question formats. Across different models and post-training methods, we find that much of the reported improvement persists even without informative audio, but largely disappears when the question format changes. These findings suggest that post-training often teaches models to exploit task-specific textual cues and answer patterns rather than developing generalizable audio understanding and reasoning capabilities.
Title:What Does LALM Post-training Learn? Rethinking Audio Understanding and Reasoning Capabilities
Abstract:Post-training often improves the performance of large audio-language models on audio understanding and reasoning benchmarks. However, these improvements may reflect adaptation to specific question formats, such as multiple-choice questions, rather than genuine advances in audio understanding and reasoning capabilities. To investigate what LALMs actually learn from post-training, we conduct controlled experiments that disentangle improvements in audio understanding from adaptation to specific question formats. Across different models and post-training methods, we find that much of the reported improvement persists even without informative audio, but largely disappears when the question format changes. These findings suggest that post-training often teaches models to exploit task-specific textual cues and answer patterns rather than developing generalizable audio understanding and reasoning capabilities.
Sound (cs.SD); Computation and Language (cs.CL); Multimedia (cs.MM); Audio and Speech Processing (eess.AS)
arXiv:2610.19476 [cs.SD]
(or arXiv:2610.19476v1 [cs.SD] for this version)
https://doi.org/10.48550/arXiv.2610.19476 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 230 words
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