InsightChain: Optimized Chain-of-Insight Analytics for LLM-driven Data Visualization

Abstract: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 rubric combining four text-based dimensions with a vision-based dimension. We assess IPM through a 100-chain human pilot and an expanded 300-chain agent-based evaluation spanning all ten domains. Experiments on public datasets show that InsightChain consistently outperforms competing prompting baselines. Existing APO methods fail to yield consistent gains on this multi-stage task, whereas VG-COPRO improves performance in both in-domain and cross-domain settings.

Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)

arXiv:2609.06438 [cs.CL]

(or arXiv:2609.06438v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.06438 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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