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专家对LLM世界模型的评估:一个高T_c超导体实例研究

Haoyu Guo¹, Maria Tikhanovskaya^2,3, Paul Raccuglia²

¹Department of Physics, Cornell University, Ithaca, NY 14853.

Proceedings of the National Academy of Sciences of the United States of America

|March 10, 2026

概括

大型语言模型 (LLM) 显示了科学文献探索的前景. 使用精选文献的检索增强生成 (RAG) 系统在回答高温杯子的复杂问题方面表现优异,与封闭模型相比.

科学领域:

材料科学材料科学材料科学
凝聚物质物理学凝聚物质物理学
计算科学计算科学

背景情况:

大型语言模型 (LLM) 为科学文献审查提供了潜力.
评估LLM在专业科学领域的准确性和深度至关重要.
高温 cuprates 作为一个复杂的测试案例LLM文献理解.

研究的目的:

评估LLM系统在专家水平上理解科学文献的能力.
为了比较各种LLM系统的性能,包括检索增强生成 (RAG),在回答复杂的特定领域问题.
开发一个强大的方法来评估科学推理中的LLM绩效.

主要方法:

创建了一个由专家策划的数据库,包含1726篇关于高温酸盐的论文.
开发了一套由专家制定的67个问题,探讨了深入的文学理解.
六个LLM系统,包括封闭模型和定制RAG系统,使用评估准确性,全面性和证据支持的标题进行了评估.

主要成果:

两个采用精选文献的检索增强生成 (RAG) 系统的表现优于封闭的LLM模型.
RAG系统在提供全面和支持良好的答案方面表现出卓越的表现.
所有评估的LLM系统都表现出有前途的能力和科学文献理解的关键缺陷.

结论:

检索增强生成 (RAG) 方法显示出在专业科学文献探索中提高LLM绩效的巨大潜力.
开发的问卷和评估标题为评估专家级LLM推理提供了有价值的工具.
需要进一步的研究来解决目前的LLM系统在科学准确性和全面性方面的局限性.

关键词:

在法学士 (LLM) 课程中.cuprate 杯拉特杯拉特是指一个杯拉特.提取增强代的恢复超导体是一种超导体.

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