Designing a Lightweight GenAI Interface for Visual Data Analysis

2025-09-02

AI Summary

Current generative AI–based data analysis systems suffer from high hallucination risk, opaque reasoning, and limited user control due to their heavy reliance on large language models. To address these issues, this paper proposes a lightweight, visualization-driven GenAI interface. It strictly confines generative AI to high-level natural language intent parsing—translating user queries into formal statistical models—while delegating core modeling, hypothesis testing, and computation to a reproducible R backend. Analysis workflows are rendered interactively via visualizations that display real-time model fitting, residual patterns, and inference pathways. Our key innovation lies in decoupling *intent understanding* from *model execution*, using visualization as a reasoning intermediary to substantially enhance transparency, user agency, and result interpretability. Experiments demonstrate that the system lowers the barrier to statistical modeling without compromising statistical rigor or end-to-end traceability.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Reasoning under Uncertainty: Relational Probabilistic Models
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

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.

Problem

• Reducing GenAI hallucination risks in data analysis
• Enhancing transparency and user control in modeling
• Integrating natural language with rigorous statistical backends

Innovation

• Hybrid system integrating GenAI with visual analytics
• GenAI translates natural language to statistical formulations
• R-based backend ensures model correctness and interpretability