MDSF：基于大语言模型的上下文感知多维数据叙事框架
MDSF: Context-Aware Multi-Dimensional Data Storytelling Framework based on Large language Model
AI总结：
本文提出基于大语言模型的多维数据叙事框架MDSF，通过预处理、增强分析和评分机制自动生成上下文感知的数据洞察与叙事，并在多个数据集上验证了其在准确性、质量和连贯性上的优势。
摘要：
数据的指数级增长和大数据技术的进步催生了对更高效、自动化数据分析与叙事方法的需求。然而，自动化数据分析系统在利用大语言模型（LLMs）进行数据洞察发现、增强分析和数据叙事方面仍面临挑战。本文介绍了基于大语言模型的多维数据叙事框架（MDSF），用于自动化洞察生成和上下文感知叙事。该框架融合了先进的预处理技术、增强分析算法以及一种独特的评分机制，以识别并优先排序可执行的洞察。使用微调后的大语言模型增强了上下文理解能力，并能在最少人工干预的情况下生成叙事。该架构还包括一种基于智能体的机制，用于实时控制叙事的延续。关键发现表明，MDSF在洞察排序准确性、描述质量和叙事连贯性方面均优于多个数据集上的现有方法。实验评估证明了MDSF能够自动化复杂分析任务、减少解释性偏差并提高用户满意度。用户研究进一步强调了其在增强内容结构、结论提取和细节丰富度方面的实际效用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
