The exponential growth of data and advancements in big data technologies have created a demand for more efficient and automated approaches to data analysis and storytelling. However, automated data analysis systems still face challenges in leveraging large language models (LLMs) for data insight discovery, augmented analysis, and data storytelling. This paper introduces the Multidimensional Data Storytelling Framework (MDSF) based on large language models for automated insight generation and context-aware storytelling. The framework incorporates advanced preprocessing techniques, augmented analysis algorithms, and a unique scoring mechanism to identify and prioritize actionable insights. The use of fine-tuned LLMs enhances contextual understanding and generates narratives with minimal manual intervention. The architecture also includes an agent-based mechanism for real-time storytelling continuation control. Key findings reveal that MDSF outperforms existing methods across various datasets in terms of insight ranking accuracy, descriptive quality, and narrative coherence. The experimental evaluation demonstrates MDSF's ability to automate complex analytical tasks, reduce interpretive biases, and improve user satisfaction. User studies further underscore its practical utility in enhancing content structure, conclusion extraction, and richness of detail.

本研究针对现有自动数据分析系统在利用大语言模型生成数据洞察及故事讲述方面的挑战，提出了一种基于大语言模型的多维数据故事讲述框架（MDSF）。该框架通过先进的预处理技术和增强分析算法，显著提高了数据洞察的生成和情境意识的故事讲述能力，实验结果显示MDSF在洞察排序准确性和叙事连贯性上均优于现有方法，具有较高的实际应用价值。

基于大语言模型的情境感知多维数据故事讲述框架