Lexara-RF: Reference-Free Metrics for Evaluating Conversational Visual Analytics Agents

Abstract:Conversational visual analytics (CVA) agents powered by large language models generate visualizations and natural-language explanations from open-ended queries. Evaluating these multimodal outputs is challenging: curated reference benchmarks are costly to author, cannot comprehensively capture the space of valid responses, and are unavailable in production. Building on the Lexara evaluation framework, we introduce Lexara-RF, a reference-free set of metrics that scores CVA outputs using only the prompt, data, and model response. We reformulate evaluation as verification: 13 metrics operationalize visualization design theory and Gricean cooperative principles as computable consistency, intent-alignment, and design validity checks. On a human-rated corpus of CVA test-cases, Lexara-RF achieves alignment comparable to reference-based formulations, outperforms surface-similarity NLG baselines, and localizes structurally grounded failures with high accuracy.

Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)

arXiv:2609.17842 [cs.HC]

(or arXiv:2609.17842v1 [cs.HC] for this version)

https://doi.org/10.48550/arXiv.2609.17842 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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