用知识图谱增强大模型，让聊天系统精准回答拓扑材料问题
Enhancing Large Language Models with Domain-Specific Knowledge: The Case in Topological Materials
- 构建拓扑材料知识图谱并融合文献，用提示学习定制对话系统
- 在结构查询、物性分析和关系推理上显著优于通用大模型
- 适合凝聚态物理研究者快速获取专业材料知识
大型语言模型（如ChatGPT）在文本生成任务中表现出色，具备理解复杂指令的能力。然而，由于领域专有语料稀缺且缺乏专门训练，通用大模型在特定领域的表现受限。此外，训练专用大规模模型需大量硬件资源，限制了研究人员的应用。为此，基于凝聚态数据中心，我们构建了材料知识图谱（MaterialsKG），并将其与文献整合。结合大语言模型与提示学习，开发出面向拓扑材料的专用对话系统TopoChat。相较于通用大模型，TopoChat在结构查询、物性分析、材料推荐及复杂关系推理方面表现更优，实现了高效精准的信息检索与知识交互，推动凝聚态材料领域的发展。
原文摘要 · Abstract (English)
Large language models (LLMs), such as ChatGPT, have demonstrated impressive performance in the text generation task, showing the ability to understand and respond to complex instructions. However, the performance of naive LLMs in speciffc domains is limited due to the scarcity of domain-speciffc corpora and specialized training. Moreover, training a specialized large-scale model necessitates signiffcant hardware resources, which restricts researchers from leveraging such models to drive advances. Hence, it is crucial to further improve and optimize LLMs to meet speciffc domain demands and enhance their scalability. Based on the condensed matter data center, we establish a material knowledge graph (MaterialsKG) and integrate it with literature. Using large language models and prompt learning, we develop a specialized dialogue system for topological materials called TopoChat. Compared to naive LLMs, TopoChat exhibits superior performance in structural and property querying, material recommendation, and complex relational reasoning. This system enables efffcient and precise retrieval of information and facilitates knowledge interaction, thereby encouraging the advancement on the ffeld of condensed matter materials.
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