# MDSF: Context-Aware Multi-Dimensional Data Storytelling Framework based on Large language Model

arXiv: https://arxiv.org/abs/2501.01014 · HTML: https://mlsanity.com/paper/2501.01014/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 67 references that are in the map.
- First posted on arXiv 2 Jan 2025.

## Builds on (most cited first, 30 of 41)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Large Language Models are not Fair Evaluators](https://mlsanity.com/paper/2305.17926/) (2023)
- [DS-Agent: Automated Data Science by Empowering Large Language Models with Case-Based Reasoning](https://mlsanity.com/paper/2402.17453/) (2024)
- [Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study](https://mlsanity.com/paper/2305.13062/) (2023)
- [Logical Natural Language Generation from Open-Domain Tables](https://mlsanity.com/paper/2004.10404/) (2020)
- [Natural Language to Code Generation in Interactive Data Science Notebooks](https://mlsanity.com/paper/2212.09248/) (2022)
- [LIDA: A Tool for Automatic Generation of Grammar-Agnostic Visualizations and Infographics using Large Language Models](https://mlsanity.com/paper/2303.02927/) (2023)
- [Data-Copilot: Bridging Billions of Data and Humans with Autonomous Workflow](https://mlsanity.com/paper/2306.07209/) (2023)
- [Table-GPT: Table-tuned GPT for Diverse Table Tasks](https://mlsanity.com/paper/2310.09263/) (2023)
- [Towards Natural Language Interfaces for Data Visualization: A Survey](https://mlsanity.com/paper/2109.03506/) (2021)
- [Chat2VIS: Generating Data Visualisations via Natural Language using ChatGPT, Codex and GPT-3 Large Language Models](https://mlsanity.com/paper/2302.02094/) (2023)
- [HTLM: Hyper-Text Pre-Training and Prompting of Language Models](https://mlsanity.com/paper/2107.06955/) (2021)
- [SheetCopilot: Bringing Software Productivity to the Next Level through Large Language Models](https://mlsanity.com/paper/2305.19308/) (2023)
- [Text2Analysis: A Benchmark of Table Question Answering with Advanced Data Analysis and Unclear Queries](https://mlsanity.com/paper/2312.13671/) (2023)
- [AI4VIS: Survey on Artificial Intelligence Approaches for Data Visualization](https://mlsanity.com/paper/2102.01330/) (2021)
- [Data Formulator: AI-powered Concept-driven Visualization Authoring](https://mlsanity.com/paper/2309.10094/) (2023)
- [InsightBench: Evaluating Business Analytics Agents Through Multi-Step Insight Generation](https://mlsanity.com/paper/2407.06423/) (2024)
- [Collecting and Characterizing Natural Language Utterances for Specifying Data Visualizations](https://mlsanity.com/paper/2110.00680/) (2021)
- [Large Language Model Augmented Narrative Driven Recommendations](https://mlsanity.com/paper/2306.02250/) (2023)
- [Calliope: Automatic Visual Data Story Generation from a Spreadsheet](https://mlsanity.com/paper/2010.09975/) (2020)
- [A Survey on ML4VIS: Applying Machine Learning Advances to Data Visualization](https://mlsanity.com/paper/2012.00467/) (2020)
- [Are Large Language Models Good Statisticians?](https://mlsanity.com/paper/2406.07815/) (2024)
- [HAIChart: Human and AI Paired Visualization System](https://mlsanity.com/paper/2406.11033/) (2024)
- [LLM4Vis: Explainable Visualization Recommendation using ChatGPT](https://mlsanity.com/paper/2310.07652/) (2023)
- [DataNarrative: Automated Data-Driven Storytelling with Visualizations and Texts](https://mlsanity.com/paper/2408.05346/) (2024)
- [From Data to Story: Towards Automatic Animated Data Video Creation with LLM-based Multi-Agent Systems](https://mlsanity.com/paper/2408.03876/) (2024)
- [Plot and Rework: Modeling Storylines for Visual Storytelling](https://mlsanity.com/paper/2105.06950/) (2021)
- [Where Are We So Far? Understanding Data Storytelling Tools from the Perspective of Human-AI Collaboration](https://mlsanity.com/paper/2309.15723/) (2023)
- [Investigating Table-to-Text Generation Capabilities of LLMs in Real-World Information Seeking Scenarios](https://mlsanity.com/paper/2305.14987/) (2023)
- [MultiVision: Designing Analytical Dashboards with Deep Learning Based Recommendation](https://mlsanity.com/paper/2107.07823/) (2021)

## Built on by (most cited first, 1 of 1)

- [Multi-Agent Multimodal Large Language Model Framework for Automated Interpretation of Fuel Efficiency Analytics in Public Transportation](https://mlsanity.com/paper/2511.13476/) (2025)

## Abstract (arXiv)

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.