Update README.md

README.md

@@ -48,4 +48,80 @@ configs:
   data_files:
   - split: test
     path: data/test-*
+license: cc-by-4.0
+task_categories:
+- question-answering
+language:
+- en
+tags:
+- music
+size_categories:
+- n<1K
 ---
+
+# HumMusQA: A Human-written Music Understanding QA Benchmark Dataset
+
+**Authors:** Benno Weck, Pablo Puentes, Andrea Poltronieri, Satyajeet Prabhu, Dmitry Bogdanov  
+
+HumMusQA is a multiple-choice question answering dataset designed to test music understanding in Large Audio-Language Models (LALMs).
+
+## Dataset Highlights
+
+- ✍️ 320 hand-written multiple-choice questions curated and validated by experts with musical training  
+- 🎵 108 Creative Commons-licensed music tracks sourced from Jamendo  
+- ⏱️ Music recordings ranging from 30 to 90 seconds  
+
+## Loading the data
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("mtg-upf/HumMusQA", split="test")
+```
+
+## Licensing
+
+- The **dataset annotations** are licensed under **Creative Commons Attribution 4.0 (CC BY 4.0)**.
+- Each **audio track** follows its **own Creative Commons license**, as specified in the dataset metadata.
+
+Users are responsible for complying with the license terms of each individual audio track.
+
+## Paper
+
+- Paper DOI: https://doi.org/10.18653/v1/2026.nlp4musa-1.9  
+- Zenodo DOI: https://doi.org/10.5281/zenodo.18462523
+- arXiv DOI: https://doi.org/10.48550/arXiv.2603.27877
+
+## Citation
+
+If you use this dataset, please cite [our paper](https://arxiv.org/abs/2603.27877):
+
+> Benno Weck, Pablo Puentes, Andrea Poltronieri, Satyajeet Prabhu, and Dmitry Bogdanov. 2026. HumMusQA: A Human-written Music Understanding QA Benchmark Dataset. In Proceedings of the 4th Workshop on NLP for Music and Audio (NLP4MusA 2026), pages 58–67, Rabat, Morocco. Association for Computational Linguistics.
+
+### BibTeX
+```bibtex
+@inproceedings{weck-etal-2026-hummusqa,
+    title = "{H}um{M}us{QA}: A Human-written Music Understanding {QA} Benchmark Dataset",
+    author = "Weck, Benno  and
+      Puentes, Pablo  and
+      Poltronieri, Andrea  and
+      Prabhu, Satyajeet  and
+      Bogdanov, Dmitry",
+    editor = "Epure, Elena V.  and
+      Oramas, Sergio  and
+      Doh, SeungHeon  and
+      Ramoneda, Pedro  and
+      Kruspe, Anna  and
+      Sordo, Mohamed",
+    booktitle = "Proceedings of the 4th Workshop on {NLP} for Music and Audio ({NLP}4{M}us{A} 2026)",
+    month = mar,
+    year = "2026",
+    address = "Rabat, Morocco",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2026.nlp4musa-1.9/",
+    doi = "10.18653/v1/2026.nlp4musa-1.9",
+    pages = "58--67",
+    ISBN = "979-8-89176-369-2",
+    abstract = "The evaluation of music understanding in Large Audio-Language Models (LALMs) requires a rigorously defined benchmark that truly tests whether models can perceive and interpret music, a standard that current data methodologies frequently fail to meet.This paper introduces a meticulously structured approach to music evaluation, proposing a new dataset of 320 hand-written questions curated and validated by experts with musical training, arguing that such focused, manual curation is superior for probing complex audio comprehension.To demonstrate the use of the dataset, we benchmark six state-of-the-art LALMs and additionally test their robustness to uni-modal shortcuts."
+}
+```