Official repository for the paper "Measuring Audio's Impact on Correctness: Audio-Contribution-Aware Post-Training of Large Audio Language Models"
- Dataset: https://huggingface.co/datasets/inclusionAI/AudioMCQ
- Paper: https://arxiv.org/abs/2509.21060
- DCASE 2025 Challenge: 1st Place Results
AudioMCQ is a comprehensive audio multiple-choice question dataset with 571k samples designed for post-training Large Audio Language Models (LALMs). The dataset features dual chain-of-thought annotations and audio-contribution filtering, achieving state-of-the-art results in audio understanding tasks.
Overview of dataset construction pipeline.
Distribution analysis of AudioMCQ dataset.
Randomly sampled questions from four distinct question types.
- 571k high-quality samples across sound, music, speech, and temporal domains
- Dual CoT annotations: Structured and unstructured reasoning paths
- Audio-Contribution filtering: Weak (54.8%) and strong (45.2%) splits
- Pre-trained models available: Weak-to-Strong and Mixed-to-Strong paradigms
For complete dataset information, statistics, data format, and download instructions, please visit:
The Hugging Face repository contains:
- Full dataset documentation
- Detailed statistics and examples
- Data format specifications
- Download links for audio files
- Usage instructions
- Model checkpoints
We provide trained model checkpoints for two post-training paradigms:
| Training Paradigm | Hugging Face Link |
|---|---|
| Weak-to-Strong | inclusionAI/AudioMCQ-Weak-To-Strong |
| Mixed-to-Strong | inclusionAI/AudioMCQ-Mixed-To-Strong |
All training code used for this project can be found in the /training_scripts directory.
- [2025.09] Paper published on arXiv
- [2025.09] AudioMCQ dataset released with 571k samples
- [2025.07] Achieved 1st place in DCASE 2025 Audio-Question-Answering challenge
- Haolin He: [email protected]
If you find AudioMCQ useful in your research, please cite:
@article{he2025audiomcq,
title={Measuring Audio's Impact on Correctness: Audio-Contribution-Aware Post-Training of Large Audio Language Models},
author={He, Haolin and others},
journal={arXiv preprint arXiv:2509.21060},
year={2025}
}We thank the organizers of DCASE 2025 and the research community for their valuable feedback and support.