Lexara: A User-Centered Toolkit for Evaluating Large Language Models for Conversational Visual Analytics
Large Language Models (LLMs) are transforming Conversational Visual Analytics (CVA) by enabling data analysis through natural language. However, evaluating LLMs for CVA remains a challenge: requiring programming expertise, overlooking real-world complexity, and lacking interpretable metrics for multi-format (visualizations and text) outputs. Through interviews with 22 CVA developers and 16 end-users, we identified use cases, evaluation criteria and workflows. We present Lexara, a user-centered evaluation toolkit for CVA that operationalizes these insights into: (i) test cases spanning real-world scenarios; (ii) interpretable metrics covering visualization quality (data fidelity, semantic ...
| Authors | Srishti Palani, Vidya Setlur |
|---|---|
| Primary category | cs.HC |
| Published | 6 March 2026 |
| Last updated at source | 6 March 2026 |
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