# MQAD: A Large-Scale Question Answering Dataset for Training Music Large Language Models Source: [MQAD: A Large-Scale Question Answering Dataset for Training Music Large Language Models]() Collection index: [llms.txt]() Data updated: 2025-10-26T05:28:46.355Z Authors: Zhihao Ouyang, Ju-Chiang Wang, Daiyu Zhang, Bin Chen, Shangjie Li, Quan Lin Published: 2025-08-27T02:05:27.000Z ## Abstract Question-answering (QA) is a natural approach for humans to understand a piece of music audio. However, for machines, accessing a large-scale dataset covering diverse aspects of music is crucial, yet challenging, due to the scarcity of publicly available music data of this type. This paper introduces MQAD, a music QA dataset built on the Million Song Dataset (MSD), encompassing a rich array of musical features, including beat, chord, key, structure, instrument, and genre -- across 270,000 tracks, featuring nearly 3 million diverse questions and captions. MQAD distinguishes itself by offering detailed time-varying musical information such as chords and sections, enabling exploration into the inherent structure of music within a song. To compile MQAD, our methodology leverages specialized Music Information Retrieval (MIR) models to extract higher-level musical features and Large Language Models (LLMs) to generate natural language QA pairs. Then, we leverage a multimodal LLM that integrates the LLaMA2 and Whisper architectures, along with novel subjective metrics to assess the performance of MQAD. In experiments, our model trained on MQAD demonstrates advancements over conventional music audio captioning approaches. The dataset and code are available at https://github.com/oyzh888/MQAD. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()