| # SceMQA | |
| [**🌐 Homepage**](https://scemqa.github.io/) | [**🤗 Dataset**](https://github.com/SceMQA/SceMQA) | [**🤗 Paper**](https://huggingface.co/papers/2402.05138) | [**📖 arXiv**](https://arxiv.org/abs/2402.05138) | [**GitHub**](https://github.com/SceMQA/SceMQA) | |
| This repo contains the evaluation code for the paper "[SceMQA: A Scientific College Entrance Level Multimodal Question Answering Benchmark]()" | |
| ## 🔔News | |
| - **🚀[2024-02-05]: Our [GitHub Code Repo](https://github.com/SceMQA/SceMQA) is now available online!** | |
| - **🔥[2024-01-25]: Our [website](https://scemqa.github.io/) is now available online! 😆** | |
| ## Introduction | |
| We introduce SceMQA, a novel benchmark for scientific multimodal question answering at the college entrance level. It addresses a critical educational phase often overlooked in existing benchmarks, spanning high school to pre-college levels. SceMQA focuses on core science subjects including Mathematics, Physics, Chemistry, and Biology. It features a blend of multiple-choice and freeresponse formats, ensuring a comprehensive evaluation of AI models’ abilities. Additionally, our benchmark provides specific knowledge | |
| points for each problem and detailed explanations for each answer. SceMQA also uniquely presents problems with identical contexts but varied questions to facilitate a more thorough and accurate assessment of reasoning capabilities. In the experiment, we evaluate both open-source and close-source state-of-the-art Multimodal Large Language Models (MLLMs), across various experimental settings. The results show that further research and development are needed in developing more capable MLLM, as highlighted by only 50% to 60% accuracy achieved by the strongest models | |
| <!-- ## Dataset Creation | |
| SceMQA was created to challenge multimodal models with tasks that demand college-level subject knowledge and deliberate reasoning, pushing the boundaries of what these models can achieve in terms of expert-level perception and reasoning. Please refer to our huggingface [**🤗 Dataset**]() for more details. --> | |
| ## Evaluation | |
| <!-- Please refer to our [eval](eval) folder for more details. --> | |
| ## 🏆 Mini-Leaderboard | |
| | Model | Setting | Val (MC) | Val (FR) | | |
| |----------------------------|---------------|:--------:|:--------:| | |
| | **Open-sourced models** | | | | | |
| | InstructBLIP-7B | | 20.48 | 12.50 | | |
| | InstructBLIP-13B | | 21.31 | 13.50 | | |
| | MiniGPT4-7B | | 21.90 | 6.50 | | |
| | MiniGPT4-13B | | 27.74 | 7.00 | | |
| | LLaVA1.5-7B | | 27.50 | 10.50 | | |
| | LLaVA1.5-13B | | 31.19 | 13.00 | | |
| | Yi-VL-6B | | 37.14 | 5.50 | | |
| | Deepseek-VL-Chat-7B | | 26.79 | 14.00 | | |
| | InternLM-XComposer2-7B | | 30.48 | 13.00 | | |
| | Qwen-VL-chat | | 26.55 | 7.00 | | |
| | **Closed-sourced models** | | | | | |
| | Google Bard | Text-only | 41.31 | - | | |
| | Gemini Pro | Text-only | 30.06 | 15.00 | | |
| | Gemini Pro | Few-shot | 38.34 | 19.50 | | |
| | Gemini Pro | Zero-shot | 41.18 | 21.50 | | |
| | GPT4-V | Text-only | 51.24 | 21.50 | | |
| | GPT4-V | Few-shot | 58.70 | 33.00 | | |
| | GPT4-V | Zero-shot | 60.83 | 36.00 | | |
| *: results provided by the authors. --> | |
| ## Disclaimers | |
| The guidelines for the annotators emphasized strict compliance with copyright and licensing rules from the initial data source, specifically avoiding materials from websites that forbid copying and redistribution. | |
| Should you encounter any data samples potentially breaching the copyright or licensing regulations of any site, we encourage you to [contact](#contact) us. Upon verification, such samples will be promptly removed. | |
| ## Contact | |
| - Zhenwen Liang: [email protected] | |
| - Xiangliang Zhang: [email protected] | |
| ## Citation | |
| **BibTeX:** | |
| ```bibtex | |
| @article{liang2024scemqa, | |
| title={SceMQA: A Scientific College Entrance Level Multimodal Question Answering Benchmark}, | |
| author={Liang, Zhenwen and Guo, Kehan and Liu, Gang and Guo, Taicheng and Zhou, Yujun and Yang, Tianyu and Jiao, Jiajun and Pi, Renjie and Zhang, Jipeng and Zhang, Xiangliang}, | |
| journal={arXiv preprint arXiv:2402.05138}, | |
| year={2024} | |
| } | |
| ``` | |