--- license: apache-2.0 task_categories: - question-answering language: - en pretty_name: Deaftest size_categories: - n<1K dataset_info: features: - name: question_id dtype: string - name: question_type_id dtype: string - name: data_type dtype: string - name: subfield dtype: string - name: question dtype: string - name: options sequence: string - name: answer dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: image_4 dtype: image - name: video_1 dtype: string - name: audio_1 dtype: audio - name: audio_2 dtype: audio - name: audio_3 dtype: audio - name: audio_4 dtype: audio splits: - name: test num_bytes: 2722106.18 num_examples: 400 download_size: 2715938 dataset_size: 2722106.18 configs: - config_name: default data_files: - split: test path: deaftest.parquet --- Official Deaftest dataset for the paper "[AV-Odyssey: Can Your Multimodal LLMs Really Understand Audio-Visual Information?]()". 🌟 For more details, please refer to the project page with data examples: [https://av-odyssey.github.io/](https://av-odyssey.github.io/). [[🌐 Webpage](https://av-odyssey.github.io/)] [[📖 Paper]()] [[🤗 Huggingface AV-Odyssey Dataset](https://huggingface.co/datasets/AV-Odyssey/AV_Odyssey_Bench)] [[🤗 Huggingface Deaftest Dataset](https://huggingface.co/datasets/AV-Odyssey/Deaftest_dataset)] [[🏆 Leaderboard](https://huggingface.co/spaces/AV-Odyssey/AV_Odyssey_Bench_Leaderboard)] --- ## 🔥 News * **`2024.11.24`** 🌟 We release AV-Odyssey, the first-ever comprehensive evaluation benchmark to explore whether MLLMs really understand audio-visual information. ## 👀 About AV-Odyssey Recently, multimodal large language models (MLLMs), such as GPT-4o, Gemini 1.5 Pro, and Reka Core, have expanded their capabilities to include vision and audio modalities. While these models demonstrate impressive performance across a wide range of audio-visual applications, our proposed **DeafTest** reveals that MLLMs often struggle with simple tasks humans find trivial: 1) determining which of two sounds is louder, and 2) determining which of two sounds has a higher pitch. Motivated by these observations, we introduce **AV-Odyssey Bench**. This benchmark encompasses **26** different tasks and **4,555** carefully crafted problems, each incorporating text, visual, and audio components. All data are **newly collected and annotated by humans**, not from any existing audio-visual dataset. AV-Odyssey Bench demonstrates three major features: 1. **Comprehensive** Audio Attributes; 2. **Extensive** Domains; 3. **Interleaved** Text, Audio, and Visual components. ## 📐 Data Examples Please refer to our project page https://av-odyssey.github.io/ for exploring more examples. ### 📍AV-Odyssey Bench