ALMs

Recent advances in Audio-Language Models have boosted performance across musical understanding benchmarks for long-form music fragments. By leveraging previous work in Music Information Retrieval, researchers have found ways to address the data scarcity issue of music-text pairs: LLMs synthesize a combination of outputs from multiple MIR models, existing metadata, and contextual knowledge into richer captions. This raises the question: to what extent does this approach provide the model with true music intelligence? Is the model aware of the content and intentions of the music, i.e., the artistic image, or does it just pretend to be? Is there a risk that GPT-generated or GPT-enhanced captions introduce hallucinations in the training data, which then leads to a drift in performance? Similar to the debate about whether models are able to truely understand the meaning of text, this one is about: how do ALMs listen, and what do they hear?

Posted: Monday, February 16, 2026.