Material Information Extraction Based on Local Large Language Model and Prompt Engineering
[Objective]This paper extracts entities and type instances of battery materials.It identifies experimental information needed to develop related materials from given research.[Methods]We utilized a locally deployed Large Language Model(LLM)and prompt engineering to transform the information extraction task into dialog-based extraction tasks without fine-tuning.We identified the relevant instance information by adding a few examples to the prompt template and allowing the LLM to provide negative answers.[Results]Without using a dataset for fine-tuning,we extracted materials entities and types with an entity recognition accuracy of 0.98,surpassing fine-tuned methods,and the material type recognition accuracy reached 0.94.[Limitations]Due to the constraints of local computational resources,the LLM's precision results in lower performance in recognizing long entities.[Conclusions]The proposed method could effectively and flexibly extract experimental information from research papers.
Large Language ModelPrompt EngineeringInformation ExtractionOrganic Battery Materials
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