# Data-Balanced Curriculum Learning for Audio Question Answering

arXiv: https://arxiv.org/abs/2507.06815 · HTML: https://mlsanity.com/paper/2507.06815/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2025.

## Built on by (most cited first, 3 of 3)

- [Omni-CLST: Error-aware Curriculum Learning with guided Selective chain-of-Thought for audio question answering](https://mlsanity.com/paper/2509.12275/) (2025)
- [Audio-Visual Intelligence in Large Foundation Models](https://mlsanity.com/paper/2605.04045/) (2026)
- [ParA-LLM: A Unified Approach to Paralinguistic and Acoustic Speech Understanding](https://mlsanity.com/paper/2609.22771/) (2026)

## Abstract (arXiv)

Audio question answering (AQA) requires models to understand acoustic content and perform complex reasoning. Current models struggle with dataset imbalances and unstable training dynamics. This work combines curriculum learning with statistical data balancing to address these challenges. The method labels question difficulty using language models, then trains progressively from easy to hard examples. Statistical filtering removes overrepresented audio categories, and guided decoding constrains outputs to valid multiple-choice formats. Experiments on the DCASE 2025 training set and five additional public datasets show that data curation improves accuracy by 11.7% over baseline models, achieving 64.2% on the DCASE 2025 benchmark.