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arXiv AI By Yu Liu, Jiahui Liu, Zhilin Liu, Cong Cao, Fangfang Yuan, Yuling Yang, Pin Xu, Yanbing Liu

Music Hallucination in Audio-Language Models: A Hierarchical Formulation and Empirical Study

Read the original on arXiv AI →

The paper presents the first music‑specific, layer‑wise empirical study of hallucination in audio‑language models, framing it as a hierarchical perceptual grounding failure across five layers: sound events, temporal properties, tonal attributes, style, and emotion. It introduces MuseDiag, a diagnostic framework that evaluates nine models and finds universal vocal misperception, significant tonal perception differences, and identifies Audio‑Flamingo‑3 as the most stable model. The study also proposes two training‑free mitigation methods, ADD‑M and TPA, which reduce hallucination in probing but show variable effectiveness in free‑form generation, highlighting the need for multi‑paradigm evaluation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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