README.md

---
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">

[![DCASE 2026 Task 5](https://img.shields.io/badge/DCASE%202026-Task%205%20Dev%20Set-red.svg)](https://dcase.community/challenge2026/task-audio-dependent-question-answering)
[![Paper](https://img.shields.io/badge/Paper-ICLR%202026-b31b1b.svg)](https://arxiv.org/abs/2509.21060)
[![Training Set](https://img.shields.io/badge/Training%20Set-AudioMCQ--StrongAC--GeminiCoT-yellow.svg)](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}
}

@article{he2026summary,
  title={Summary of DCASE 2026 Task 5: Audio-Dependent Question Answering},
  author={He, Haolin and Sun, Renhe and Dai, Zheqi and Du, Xingjian and Wu, Chunyat and Liang, Zining and Liu, Zhengxi and Lei, Jiahe and Wang, Runbang and Zhou, Jiayi and Yang, Mingru and Li, Xiquan and Chen, Yun and Chen, Xie and Duan, Zhiyao and Wang, Weiqiang and Plumbley, Mark D. and Liu, Jian and Kong, Qiuqiang},
  journal={arXiv preprint arXiv:2607.18718},
  year={2026}
}
```