用自然语言生成定制化数据洞察和动态图表，提升分析效率。
Text2Insight: Transform natural language text into insights seamlessly using multi-model architecture
- 多模型架构融合Llama3、NER与图表预测，精准理解用户需求。
- 自然语言转查询准确率达99%，生成图表与洞察符合用户预期。
- 适合非技术用户快速获取数据洞见，尤其适用于医疗金融场景。
随着医疗、金融、科研等领域对动态、以用户为中心的数据分析与可视化需求增长，传统静态预设工具难以满足个性化需求。为此，本文提出Text2Insight，一种基于多模型架构的创新解决方案，可根据用户自然语言指令生成定制化数据分析与可视化结果。系统首先解析数据集结构，利用预训练Llama3模型将自然语言查询转为SQL，再通过命名实体识别（NER）模型优化准确性；图表预测模块选择最优可视化类型，Llama3基于查询结果生成洞察。系统还集成基于BERT框架的问答模型与预测模型，支持历史分析与未来趋势预测。性能评估显示，文本转查询任务达到99%准确率、100%精确率、99%召回率与99%F1分数，BLEU得分为0.5；问答模型准确率为89%，预测模型达70%。结果验证了Text2Insight在生成动态、个性化数据洞察方面的有效性与可行性。
原文摘要 · Abstract (English)
The growing demand for dynamic, user-centric data analysis and visualization is evident across domains like healthcare, finance, and research. Traditional visualization tools often fail to meet individual user needs due to their static and predefined nature. To address this gap, Text2Insight is introduced as an innovative solution that delivers customized data analysis and visualizations based on user-defined natural language requirements. Leveraging a multi-model architecture, Text2Insight transforms user inputs into actionable insights and dynamic visualizations. The methodology begins with analyzing the input dataset to extract structural details such as columns and values. A pre-trained Llama3 model converts the user's natural language query into an SQL query, which is further refined using a Named Entity Recognition (NER) model for accuracy. A chart predictor determines the most suitable visualization type, while the Llama3 model generates insights based on the SQL query's results. The output is a user-friendly and visually informative chart. To enhance analysis capabilities, the system integrates a question-answering model and a predictive model using the BERT framework. These models provide insights into historical data and predict future trends. Performance evaluation of Text2Insight demonstrates its effectiveness, achieving high accuracy (99%), precision (100%), recall (99%), and F1-score (99%), with a BLEU score of 0.5. The question-answering model attained an accuracy of 89% and the predictive model achieved 70% accuracy. These results validate Text2Insight as a robust and viable solution for transforming natural language text into dynamic, user-specific data analysis and visualizations.
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