Designing a Lightweight GenAI Interface for Visual Data Analysis
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914018649702400 |
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| author | Koonchanok, Ratanond Kale, Alex Reda, Khairi |
| author_facet | Koonchanok, Ratanond Kale, Alex Reda, Khairi |
| contents | Recent advances in Generative AI have transformed how users interact with data analysis through natural language interfaces. However, many systems rely too heavily on LLMs, creating risks of hallucination, opaque reasoning, and reduced user control. We present a hybrid visual analysis system that integrates GenAI in a constrained, high-level role to support statistical modeling while preserving transparency and user agency. GenAI translates natural language intent into formal statistical formulations, while interactive visualizations surface model behavior, residual patterns, and hypothesis comparisons to guide iterative exploration. Model fitting, diagnostics, and hypothesis testing are delegated entirely to a structured R-based backend, ensuring correctness, interpretability, and reproducibility. By combining GenAI-assisted intent translation with visualization-driven reasoning, our approach broadens access to modeling tools without compromising rigor. We present an example use case of the tool and discuss challenges and opportunities for future research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_02878 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Designing a Lightweight GenAI Interface for Visual Data Analysis Koonchanok, Ratanond Kale, Alex Reda, Khairi Human-Computer Interaction Recent advances in Generative AI have transformed how users interact with data analysis through natural language interfaces. However, many systems rely too heavily on LLMs, creating risks of hallucination, opaque reasoning, and reduced user control. We present a hybrid visual analysis system that integrates GenAI in a constrained, high-level role to support statistical modeling while preserving transparency and user agency. GenAI translates natural language intent into formal statistical formulations, while interactive visualizations surface model behavior, residual patterns, and hypothesis comparisons to guide iterative exploration. Model fitting, diagnostics, and hypothesis testing are delegated entirely to a structured R-based backend, ensuring correctness, interpretability, and reproducibility. By combining GenAI-assisted intent translation with visualization-driven reasoning, our approach broadens access to modeling tools without compromising rigor. We present an example use case of the tool and discuss challenges and opportunities for future research. |
| title | Designing a Lightweight GenAI Interface for Visual Data Analysis |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2509.02878 |