Lexara-RF: Reference-Free Metrics for Evaluating Conversational Visual Analytics Agents
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, inte...
| Authors | Srishti Palani, Vidya Setlur |
|---|---|
| Primary category | cs.HC |
| Published | 15 September 2026 |
| Last updated at source | 15 September 2026 |
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