Haozy/SceMQA-main

SceMQA

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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 is now available online!

• 🔥[2024-01-25]: Our website 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

Evaluation

🏆 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 us. Upon verification, such samples will be promptly removed.

Contact

• Zhenwen Liang: zliang6@nd.edu
• Xiangliang Zhang: xzhang33@nd.edu

Citation

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}
}