InsightChain: Optimized Chain-of-Insight Analytics for LLM-driven Data Visualization
Large language models (LLMs) are increasingly used for automated data visualization, yet existing approaches often frame visualization generation as a single-step mapping from user query to figure or code, overlooking the iterative analytical reasoning process of expert analysts. We present InsightChain, a four-stage visualization prompting pipeline (Explore--Focus--Test--Present) that emulates expert analytical workflows, together with VG-COPRO, a vision-guided automatic prompt optimization (APO) method adapted to jointly optimize such multi-stage, executable pipelines. To address the evaluation gap for complex data visualization, we introduce the Insight Progression Metric (IPM), a rubr...
| Authors | Hanya Sun, Chen Zhang, Sheng Liang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 6 September 2026 |
| Last updated at source | 6 September 2026 |
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