摘要
随着大语言模型在自然语言处理领域的广泛应用,其在数学建模领域中的准确性成为研究的焦点。该文通过引入命名实体识别(NER)和提示词工程(Prompt Engineering)的方法,提高大语言模型在数学建模问题中的回答准确率。基于GPT-3.5模型,通过对数学建模领域特定问题的语境和结构进行深入分析,阐述一套有效的NER策略和提示词工程方法,以优化大模型对数学问题的理解和响应。
With the wide application of large language model in the field of natural language processing,its accuracy in the field of mathematical modeling has become the focus of research.In this paper,named entity recognition(NER)and prompt engineering are introduced to improve the accuracy of large language model in mathematical modeling.Based on the GPT-3.5 model,through the in-depth analysis of the context and structure of specific problems in the field of mathematical modeling,this paper expounds a set of effective NER strategies and prompt engineering methods to optimize the understanding and response of the large model to mathematical problems.
出处
《科技创新与应用》
2024年第22期18-21,共4页
Technology Innovation and Application
基金
太原科技大学校级大学生创新创业训练计划项目(XJ2023181)。
关键词
大语言模型
ChatGPT
命名实体识别
提示词工程
数学建模
large language model
ChatGPT
named entity recognition
prompt word engineering
mathematical modeling
