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README.md

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----
-license: apache-2.0
----
+---
+configs:
+- config_name: default
+  data_files:
+  - split: dev
+    path: "dev.jsonl"
+license: apache-2.0
+---
+# [DCASE 2026 Challenge] Task 5 Development Set: Audio-Dependent Question Answering (ADQA)
+
+<div align="center">
+
+[![DCASE 2026](https://img.shields.io/badge/DCASE%202026-Task%205%20Dev%20Set-red.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/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/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 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
+@inproceedings{he2025audiomcq,
+  title={Measuring Audio's Impact on Correctness: Audio-Contribution-Aware Post-Training of Large Audio Language Models},
+  author={He, Haolin and others},
+  booktitle={Proceedings of the International Conference on Learning Representations (ICLR)},
+  year={2026}
+}
+```