Text-to-Visualization (Text2Vis) aims to democratize data insights for non-expert users by transforming natural language query (NLQ) into visualization specification (VS). In view of the high dependence of rule-based methods on predefined VS templates and poor NLQ understanding ability of small data-driven methods, recent works resort to leveraging pre-trained LLMs to perform NLQ-understanding and VS-generating in Text2Vis tasks via prompt-guided in-context learning. However, existing LLM-based methods still fall short of satisfactory end-to-end Text2Vis performances primarily owing to the limited ability of pre-trained LLMs in directly retrieving and operating NLQ-intended tabular data. Inspired by the SQL generating ability born with latest LLMs, this paper proposes the idea of harnessing LLMs for SQL-driven visualization data retrieval and operation. Nonetheless, there remains a nonneglectable gap between data visualization queries in Text2Vis tasks and SQL retrieving queries in LLM corpus. To fill the gap, this paper proposes a Visualization Query to SQL (VisQ2SQL) framework to obtain NLQ-intended data, primarily by fine-tuning LLMs through preference learning data-retrieval SQLs induced from VS and those generated by LLMs. We conduct extensive experiments to demonstrate the superiority of VisQ2SQL over SOTA methods, and various ablation studies to verify the efficacy of VisQ2SQL.

Text-to-Visualization (Text2Vis) generates data visualizations directly from natural language queries, democratizing access to data insights. Early Text2Vis efforts, primarily relying on rule-based systems and machine learning models, struggled to handle semantically intricate queries. The advent of large language models (LLMs) allows for better generalization in generating visualization code. However, LLM-based approaches have mainly focused on textual or code-level optimizations, neglecting the potential benefits of assessing and improving visualized charts. Hence, we propose Visualization Refinement (VisRef), a novel framework based on vision-language models (VLMs) to enhance Text2Vis outputs. (1) Knowledge Extraction -- VisRef extracts visualization assessment knowledge through a hierarchical contrastive prompt and multi-granularity quality assessment framework by comparing superior ground-truth charts with inferior Text2Vis outputs; and (2) VLM Fine-Tuning -- This knowledge is used to fine-tune a VLM through a two-stage approach, including warm-up and iterative preference alignment phases, to judge visualization quality and provide code-level refinement suggestions. Experimental results demonstrate that VisRef significantly outperforms state-of-the-art approaches, including LLM-based and VLM-prompted, and exhibits strong orthogonal compatibility with existing approaches.