Enhancing Large Language Models with Domain-Specific Knowledge: The Case in Topological Materials
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917137622237184 |
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| author | Xu, HuangChao Zhang, Baohua Jin, Zhong Zhu, Tiannian Wu, Quansheng Weng, Hongming |
| author_facet | Xu, HuangChao Zhang, Baohua Jin, Zhong Zhu, Tiannian Wu, Quansheng Weng, Hongming |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_13732 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Large Language Models with Domain-Specific Knowledge: The Case in Topological Materials Xu, HuangChao Zhang, Baohua Jin, Zhong Zhu, Tiannian Wu, Quansheng Weng, Hongming Computation and Language Materials Science Machine Learning 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. |
| title | Enhancing Large Language Models with Domain-Specific Knowledge: The Case in Topological Materials |
| topic | Computation and Language Materials Science Machine Learning |
| url | https://arxiv.org/abs/2409.13732 |