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configs:
- config_name: default
data_files:
- split: dev
path: "dev.jsonl"
license: apache-2.0
---
# DCASE 2026 Task 5 Audio-Dependent Question Answering (ADQA) Development Set
<div align="center">
[](https://dcase.community/challenge2026/task-audio-dependent-question-answering)
[](https://arxiv.org/abs/2509.21060)
[](https://huggingface.co/datasets/Harland/AudioMCQ-StrongAC-GeminiCoT)
</div>
This is the official **Development Set** for [DCASE 2026 Challenge Task 5: Audio-Dependent Question Answering (ADQA)](https://dcase.community/challenge2026/task-audio-dependent-question-answering).
The ADQA task focuses on addressing **"Textual Hallucination"** in Large Audio-Language Models (LALMs) — where models pass audio understanding benchmarks by relying on text prompts and internal linguistic priors rather than actual audio perception. ADQA introduces a rigorous evaluation framework using **Audio-Dependency Filtering (ADF)** to ensure questions cannot be answered through common sense or text-only reasoning.
## Audio-Dependency Filtering (ADF)
All samples in this development set undergo a rigorous four-step ADF hard-filtering process to guarantee genuine audio dependence:
1. **Silent Audio Filtering:** Questions solvable by LALMs without audio are removed.
2. **LLM Common-sense Check:** Ensures no external knowledge alone can solve the question.
3. **Perplexity-based Soft Filtering:** Eliminates samples with text-based statistical shortcuts.
4. **Manual Verification:** Final human-in-the-loop check for ground-truth accuracy.
## Statistics
| Metric | Count |
|--------|-------|
| Total Samples | 1,607 |
| Unique Audio Files | 1,607 |
### Data Sources
The development set is composed of two parts:
- **Existing Benchmarks:** A portion of the samples is derived from established audio understanding benchmarks, including [MMAU](https://github.com/sakshi113/mmau), [MMAR](https://github.com/ddlBoJack/MMAR), and [MMSU](https://huggingface.co/datasets/ddwang2000/MMSU). These samples cover a wide range of audio understanding tasks such as speech, music, and sound perception.
- **Human-Annotated Questions:** The remaining majority consists of newly constructed, human-annotated multiple-choice questions based on diverse audio sources, designed to further challenge models on real-world audio comprehension.
All samples undergo the four-step **Audio-Dependency Filtering (ADF)** process described above.
## Directory Structure
```text
DCASE2026-Task5-DevSet/
├── dev.jsonl # Main data file (1,607 samples, shuffled)
├── dev_audios/ # Audio files (1,607 .wav files)
└── README.md
```
## Data Format
Each entry in `dev.jsonl` is a JSON object with the following fields:
| Field | Type | Description |
|-------|------|-------------|
| `id` | string | Unique sample identifier (e.g., `dev_0001`) |
| `audio_path` | string | Relative path to audio file |
| `question_text` | string | Question text |
| `answer` | string | Correct answer |
| `multi_choice` | list[string] | Answer choices |
### Example
```json
{
"id": "dev_0001",
"audio_path": "dev_audios/dev_0001.wav",
"question_text": "What is the speaker's primary emotion in this audio?",
"answer": "Happiness",
"multi_choice": ["Sadness", "Happiness", "Anger", "Fear"]
}
```
## Submission Format
The system output file should be a `.csv` file with the following two columns:
| Column | Description |
|--------|-------------|
| `question` | The question ID (e.g., `dev_0001`) |
| `answer` | The system's answer, must match one of the given choices |
## License
This dataset is distributed under the **Apache-2.0** license.
## Citation
If you use this development set or participate in DCASE 2026 Task 5, please cite:
```bibtex
@article{he2025measuring,
title={Measuring Audio's Impact on Correctness: Audio-Contribution-Aware Post-Training of Large Audio Language Models},
author={He, Haolin and Du, Xingjian and Sun, Renhe and Dai, Zheqi and Xiao, Yujia and Yang, Mingru and Zhou, Jiayi and Li, Xiquan and Liu, Zhengxi and Liang, Zining and others},
journal={arXiv preprint arXiv:2509.21060},
year={2025}
}
```
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