Research on an Automated Data Insight Generation Method Based on Large Language Models
DOI:
https://doi.org/10.70393/6a69656173.333436ARK:
https://n2t.net/ark:/40704/JIEAS.v3n6a02Disciplines:
Artificial Intelligence TechnologySubjects:
Natural Language ProcessingReferences:
18Keywords:
Large Language Models, Automated Data Insights, Deep Learning, Natural Language Processing, Data Mining, Machine LearningAbstract
This study aims to explore automated data insight generation methods based on large language models (LLMs), and systematically analyzes the application potential and challenges of LLMs in the field of data insights. Starting from an overview of LLMs and their development, it expounds the theoretical foundations and technological evolution of LLMs in natural language processing. Then, the research method and experimental scheme are elaborately designed, and empirical studies are conducted using deep learning frameworks and large-scale datasets. Experimental results show that automated data insight generation methods based on LLMs exhibit significant advantages in data understanding, pattern recognition, and information extraction, effectively improving the accuracy and efficiency of data insights. Through multi-dimensional analysis of the experimental results, the study reveals the unique advantages and limitations of this method in handling complex data structures and high-dimensional data. Furthermore, the study discusses the theoretical mechanisms and technical bottlenecks behind the results, and proposes concrete strategies for optimizing model performance and expanding application scenarios. Finally, this paper summarizes the research findings and looks ahead to future research directions, with the aim of providing theoretical support and technical references for the further development of automated data insight generation.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
