---
configs:
- config_name: default
  data_files:
  - split: eval
    path: "eval-noanswer.jsonl"
license: apache-2.0
---
# ADQA-Bench: Audio-Dependent Question Answering Evaluation Benchmark

<div align="center">

[![ADQA-Bench](https://img.shields.io/badge/ADQA--Bench-Evaluation%20Benchmark-blue.svg)](https://dcase.community/challenge2026/index#task5)
[![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 **Evaluation Set** for [DCASE 2026 Challenge Task 5: Audio-Dependent Question Answering (ADQA)](https://dcase.community/challenge2026/index#task5).

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 evaluation 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 | 3,000 |
| Unique Audio Files | 3,000 |

### Data Sources

The evaluation 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 portion 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
ADQA-Bench/
├── eval-noanswer.jsonl       # Evaluation data without answers (3,000 samples)
├── eval.jsonl                # Full data with answers (to be released after the competition)
├── eval_audios/              # Audio files (3,000 .wav files)
└── README.md
```

## Data Format

Each entry in `eval-noanswer.jsonl` is a JSON object with the following fields:

| Field | Type | Description |
|-------|------|-------------|
| `id` | string | Unique sample identifier (e.g., `eval_0001`) |
| `audio_path` | string | Relative path to audio file |
| `question_text` | string | Question text |
| `multi_choice` | list[string] | Answer choices |

### Example

```json
{
  "id": "eval_0001",
  "audio_path": "eval_audios/eval_0001.wav",
  "question_text": "What is the speaker's primary emotion in this audio?",
  "multi_choice": ["Sadness", "Happiness", "Anger", "Fear"]
}
```

## License

This dataset is distributed under the **Apache-2.0** license.

## Citation

If you use this evaluation 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}
}
```