标题： OPENXRD：LLM/MLLM XRD问答的全面基准和增强框架 显示英文标题

标题： OPENXRD: A Comprehensive Benchmark and Enhancement Framework for LLM/MLLM XRD Question Answering

摘要： 这项工作介绍了OPENXRD，这是一个为晶体学问答设计的开放书管道，它将文本提示与由GPT-4.5生成的简洁支持内容相结合。 与可能引发版权问题的扫描教科书不同，OPENXRD生成紧凑的、领域特定的参考内容，帮助小型模型理解X射线衍射（XRD）中的关键概念。 我们通过比较不同的视觉语言模型，包括GPT-4和基于LLaVA的框架如Mistral、LLaMA和QWEN，在封闭书（无支持材料）和开放书（有支持材料）条件下，在一个定义明确的217个专家级XRD问题集上评估了OPENXRD。 我们的实验结果表明，使用GPT-4.5生成摘要的模型在准确性方面有显著提升，尤其是那些在晶体学方面训练有限的模型。 OPENXRD利用大型模型的知识来填补晶体学中的知识空白，并表明AI生成的文本可以帮助小型模型在科学任务中更有效地推理。 虽然当前版本的OPENXRD专注于基于文本的输入，但我们还探索了未来的扩展，例如添加真实的晶体图或衍射图以在专门的材料科学背景下提高解释能力。 总体而言，OPENXRD表明专用的开放书系统在材料科学中有用，并为关键科学领域的更广泛的自然语言处理（NLP）工具提供了基础。 显示英文摘要

摘要： This work presents OPENXRD, an open-book pipeline designed for crystallography question answering, which integrates textual prompts with concise supporting content generated by GPT-4.5. Instead of using scanned textbooks, which may lead to copyright issues, OPENXRD generates compact, domain-specific references that help smaller models understand key concepts in X-ray diffraction (XRD). We evaluate OPENXRD on a well-defined set of 217 expert-level XRD questions by comparing different vision-language models, including GPT-4 and LLaVA-based frameworks such as Mistral, LLaMA, and QWEN, under both closed-book (without supporting material) and open-book (with supporting material) conditions. Our experimental results show significant accuracy improvements in models that use the GPT-4.5-generated summaries, particularly those with limited prior training in crystallography. OPENXRD uses knowledge from larger models to fill knowledge gaps in crystallography and shows that AI-generated texts can help smaller models reason more effectively in scientific tasks. While the current version of OPENXRD focuses on text-based inputs, we also explore future extensions such as adding real crystal diagrams or diffraction patterns to improve interpretation in specialized materials science contexts. Overall, OPENXRD shows that specialized open-book systems can be useful in materials science and provides a foundation for broader natural language processing (NLP) tools in critical scientific fields.