AI RESEARCH
Assessing Factual Music Comprehension in Large Audio Language Models
arXiv CS.LG
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ArXi:2511.05550v2 Announce Type: replace-cross Large audio language models (LALMs) leverage multimodal representations to generate open-ended answers to natural language queries about audio. In this paper, we (1) provide empirical evidence that assessment of LALMs using the popular MusicQA dataset fails to measure whether a model's responses about music are factually correct, and (2) develop a new protocol for assessing the music comprehension capabilities of LALMs.