An LLM-orchestrated multi-agent framework for end-to-end BDaaS automation with drift awareness is proposed and evaluated on tabular benchmarks for improved lifecycle reliability over baselines.
Lida: A tool for automatic generation of grammar-agnostic visualizations and infographics using large language models.arXiv preprint arXiv:2303.02927
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Introduces CVLAT and VFRI to disentangle visual vs factual correctness in 15 LVLMs, classifies models by reliance sign, compares to human baseline, and tests prompt interventions.
chart-plot is an agentic harness using style-aware code generation from venue figures, a LaTeX-aware render-and-revise loop, and structured edit handles to produce top-venue-ready academic charts.
An LLM-based framework recommends drill-down paths in visual analytics by approximating a greedy algorithm, interpreting user intent, and managing exploration branches to reduce cognitive load.
Generative interfaces let LLMs create task-specific UIs that users prefer up to 72% more than standard chat responses across tested tasks.
citing papers explorer
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Trustworthy Self-Composable Big-Data-as-a-Service: An LLM-Orchestrated Multi-Agent Framework for Automated Data Engineering, AutoML, MLOps Deployment, and Drift-Aware Lifecycle Optimization
An LLM-orchestrated multi-agent framework for end-to-end BDaaS automation with drift awareness is proposed and evaluated on tabular benchmarks for improved lifecycle reliability over baselines.
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Disentangling Visual and Factual Correctness in LVLMs' Visualization Literacy
Introduces CVLAT and VFRI to disentangle visual vs factual correctness in 15 LVLMs, classifies models by reliance sign, compares to human baseline, and tests prompt interventions.
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Demonstrating chart-plot: Closing the Last Mile of Academic Chart Generation
chart-plot is an agentic harness using style-aware code generation from venue figures, a LaTeX-aware render-and-revise loop, and structured edit handles to produce top-venue-ready academic charts.
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Intelligent Drill-Down: Large Language Model-Driven Drill-Down Technique for Human-AI Collaborative Visual Exploration
An LLM-based framework recommends drill-down paths in visual analytics by approximating a greedy algorithm, interpreting user intent, and managing exploration branches to reduce cognitive load.
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Generative Interfaces for Language Models
Generative interfaces let LLMs create task-specific UIs that users prefer up to 72% more than standard chat responses across tested tasks.