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OPENXRD: A Comprehensive Benchmark Framework for LLM/MLLM XRD Question Answering
arXiv – CS AI|Ali Vosoughi, Ayoub Shahnazari, Yufeng Xi, Zeliang Zhang, Griffin Hess, Chenliang Xu, Niaz Abdolrahim|
🤖AI Summary
Researchers introduced OPENXRD, a comprehensive benchmarking framework for evaluating large language models and multimodal LLMs in crystallography question answering. The study tested 74 state-of-the-art models and found that mid-sized models (7B-70B parameters) benefit most from contextual materials, while very large models often show saturation or interference.
Key Takeaways
- →OPENXRD framework includes 217 expert-curated X-ray diffraction questions covering fundamental to advanced crystallographic concepts.
- →Mid-sized models (7B-70B parameters) showed the largest gains from contextual materials compared to very large models.
- →Expert-reviewed materials provided significantly higher improvements than AI-generated ones, emphasizing content quality over quantity.
- →The framework tests both closed-book and open-book conditions to measure context assimilation capabilities.
- →74 state-of-the-art models were benchmarked including GPT-4, GPT-5, O-series, LLaVA, LLaMA, QWEN, Mistral, and Gemini families.
Mentioned in AI
Models
GPT-4OpenAI
GPT-4.5OpenAI
GPT-5OpenAI
GeminiGoogle
Read Original →via arXiv – CS AI
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