mitea123/matQnA
收藏Hugging Face2026-04-07 更新2026-04-12 收录
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资源简介：
---
license: mit
task_categories:
- image-text-to-text
tags:
- multimodal
- materials-science
- question-answering
---
# MatQnA: A Benchmark Dataset for Multi-modal Large Language Models in Materials Characterization and Analysis
This repository hosts the MatQnA dataset, a multi-modal benchmark dataset presented in the paper [MatQnA: A Benchmark Dataset for Multi-modal Large Language Models in Materials Characterization and Analysis](https://huggingface.co/papers/2509.11335).
MatQnA is specifically designed to evaluate the capabilities of AI models in the specialized field of materials characterization and analysis. It includes data from ten mainstream characterization methods, such as X-ray Photoelectron Spectroscopy (XPS), X-ray Diffraction (XRD), Scanning Electron Microscopy (SEM), and Transmission Electron Microscopy (TEM).
The dataset comprises high-quality question-answer pairs, incorporating both multiple-choice and subjective questions, developed using a hybrid approach combining LLMs with human-in-the-loop validation. It serves as a crucial resource for systematically validating and advancing multi-modal AI models in scientific research scenarios related to materials.
提供机构：
mitea123
