Intelligent Multi-Model Visualisation Framework Using Large Language Models

Abstract

The growth and spread of huge, non-homogeneous datasets in areas like healthcare, finance, and education has also created significant difficulties in providing data in a way that is useful enough to be accessible to non-expert stakeholders. Traditional methods of visualization, such as fixed dashboard displays and ready-made charts, have been sufficient as a communication mechanism to trained analysts but often fail when communicating subtle information to users not prepared in the formal data science. This paper introduces Intelligent Multi-Modal Visualization Framework using Large Language Models (IMVF-LLM), an end-to-end model that combines multimodal data integration, chain-of-thought style reasoning that relies on Large Language Models and automated declarative visualization generation to convert heterogeneous data sets into coherent human-readable narrative reports. The framework relies on the state-of-the-art vision-language models including GPT-4, CLIP, Flamingo, BLIP-2, and MiniGPT-4 to do alignment between the vision and the visualization synthesis. An organizational user study of 214 subjects, when controlled showed an increase by 71 percent in data understanding and an 18 percent enhancement in information storage as compared to either a visual or textual display. Experimental assessment also provided 22 per cent enhancement in the cross-modal retrieval accuracy and 92 per cent fidelity in generated Vega-Lite specifications over human generated counterparts. Taken together, these results justify the enhanced generalizability of multimodal storytelling that is based on LLM to analyze data in a variety of real-life applications.

Introduction

In modern industries like healthcare, finance, and education, massive amounts of heterogeneous data are generated, but conventional visualization tools are limited to technical users and fail to provide intuitive insights. The Intelligent Multi-Modal Visualization Framework using Large Language Models (IMVF-LLM) addresses this gap by combining multimodal data fusion, LLM-based reasoning, and automated visualization generation to produce interactive, interpretable stories from text, images, audio, and structured data.

The system processes heterogeneous inputs through modality-specific encoders, aligns them in a shared latent space, and uses a GPT-4 reasoning engine with chain-of-thought prompting to extract insights. Visualizations are generated in Vega-Lite via Altair, supporting narrative storytelling and user interaction through natural language queries. This end-to-end framework democratizes data analytics, enabling users of all technical levels to interact with complex data, while addressing limitations of existing vision-language models, including semantic gaps, low interpretability, and lack of automated insight generation.

Implementation leverages PyTorch 2.0, HuggingFace Transformers, BLIP-2 visual encoders, and NVIDIA A100 GPUs, tested on synthetic and real-world multimodal datasets, achieving a responsive and interpretable visualization pipeline.

Conclusion

The paper has introduced IMVF-LLM, a multi-modal visualization platform that combines advanced multimodal fusion algorithms with reasoning based on large language models in order to introduce an automated system that generates meaningful visualizations on heterogeneous sources of data. Through generating contextual narratives through textual semantics and visual and structured data, the framework brings to light patterns hidden in raw data, and the process of interpreting data takes data interpretation to another level beyond the constraints of manual curation. The outcome of experimental results and user studies ensure that IMVF-LLM is significantly more effective than unimodal and purely visual methods, in terms of comprehension, retention, and interpretability measures. The ability of the framework to match visualizations to the characteristics of queries, expose surface anomalies through the intuitive visualizations, and show causal relationships makes the framework a meaningful addition to scalable and human-friendly data analytics. Future research on real-time processing, explainability, and ethical robustness will further cement the use of this framework to other areas of real-world, providing tools that are useful to analysts to substantively enhance analytical reasoning of multi-faceted and complex data.

References

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Copyright

Copyright © 2026 Rupsa Das, Nainsi Jaiswal , Tejasvini Ghadage, B. Pooja, Trupti Cariappa, Prof. Debasmita Datta. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.