@inproceedings{koh-etal-2026-jamendo,
title = "Jamendo-{MT}-{QA}: A Benchmark for Multi-Track Comparative Music Question Answering",
author = "Koh, Junyoung and
Lee, Jaeyun and
Kim, Soo Yong and
Choi, Gyu Hyeong and
Koh, Jung In and
Phillips, Jordan and
Lee, Yeonjin and
Song, Min",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.125/",
pages = "2612--2631",
ISBN = "979-8-89176-395-1",
abstract = "Recent work on music question answering (Music-QA) has primarily focused on single-track understanding, where models answer questions about an individual audio clip using its tags, captions, or metadata. However, listeners often describe music in comparative terms, and existing benchmarks do not systematically evaluate reasoning across multiple tracks. Building on the Jamendo-QA dataset, we introduce Jamendo-MT-QA, a dataset and benchmark for multi-track comparative question answering. From Creative Commons-licensed tracks on Jamendo, we construct 36,519 comparative QA items over 12,173 track pairs, with each pair yielding three question types: yes/no, short-answer, and sentence-level questions. We describe an LLM-assisted pipeline for generating and filtering comparative questions, and benchmark representative audio-language models using both automatic metrics and LLM-as-a-Judge evaluation."
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<abstract>Recent work on music question answering (Music-QA) has primarily focused on single-track understanding, where models answer questions about an individual audio clip using its tags, captions, or metadata. However, listeners often describe music in comparative terms, and existing benchmarks do not systematically evaluate reasoning across multiple tracks. Building on the Jamendo-QA dataset, we introduce Jamendo-MT-QA, a dataset and benchmark for multi-track comparative question answering. From Creative Commons-licensed tracks on Jamendo, we construct 36,519 comparative QA items over 12,173 track pairs, with each pair yielding three question types: yes/no, short-answer, and sentence-level questions. We describe an LLM-assisted pipeline for generating and filtering comparative questions, and benchmark representative audio-language models using both automatic metrics and LLM-as-a-Judge evaluation.</abstract>
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%0 Conference Proceedings
%T Jamendo-MT-QA: A Benchmark for Multi-Track Comparative Music Question Answering
%A Koh, Junyoung
%A Lee, Jaeyun
%A Kim, Soo Yong
%A Choi, Gyu Hyeong
%A Koh, Jung In
%A Phillips, Jordan
%A Lee, Yeonjin
%A Song, Min
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F koh-etal-2026-jamendo
%X Recent work on music question answering (Music-QA) has primarily focused on single-track understanding, where models answer questions about an individual audio clip using its tags, captions, or metadata. However, listeners often describe music in comparative terms, and existing benchmarks do not systematically evaluate reasoning across multiple tracks. Building on the Jamendo-QA dataset, we introduce Jamendo-MT-QA, a dataset and benchmark for multi-track comparative question answering. From Creative Commons-licensed tracks on Jamendo, we construct 36,519 comparative QA items over 12,173 track pairs, with each pair yielding three question types: yes/no, short-answer, and sentence-level questions. We describe an LLM-assisted pipeline for generating and filtering comparative questions, and benchmark representative audio-language models using both automatic metrics and LLM-as-a-Judge evaluation.
%U https://aclanthology.org/2026.findings-acl.125/
%P 2612-2631
Markdown (Informal)
[Jamendo-MT-QA: A Benchmark for Multi-Track Comparative Music Question Answering](https://aclanthology.org/2026.findings-acl.125/) (Koh et al., Findings 2026)
ACL
- Junyoung Koh, Jaeyun Lee, Soo Yong Kim, Gyu Hyeong Choi, Jung In Koh, Jordan Phillips, Yeonjin Lee, and Min Song. 2026. Jamendo-MT-QA: A Benchmark for Multi-Track Comparative Music Question Answering. In Findings of the Association for Computational Linguistics: ACL 2026, pages 2612–2631, San Diego, California, United States. Association for Computational Linguistics.