We report on a systematic, PRISMA-guided survey of research at the intersection of LLMs and visualization, with a particular focus on visio-verbal interaction -- where verbal and visual modalities converge to support data sense-making. The emergence of Large Language Models (LLMs) has introduced new paradigms for interacting with data visualizations through natural language, leading to intuitive, multimodal, and accessible interfaces. We analyze 48 papers across six dimensions: application domain, visualization task, visualization representation, interaction modality, LLM integration, and system evaluation. Our classification framework maps LLM roles across the visualization pipeline, from data querying and transformation to visualization generation, explanation, and navigation. We highlight emerging design patterns, identify gaps in accessibility and visualization reading, and discuss the limitations of current LLMs in spatial reasoning and contextual grounding. We further reflect on evaluations of combined LLM-visualization systems, highlighting how current research projects tackle this challenge and discuss current gaps in conducting meaningful evaluations of such systems. With our survey we aim to guide future research and system design in LLM-enhanced visualization, supporting broad audiences and intelligent, conversational interfaces.

翻译：本文通过一项遵循PRISMA指南的系统性综述，探讨了大语言模型（LLMs）与可视化交叉领域的研究，特别聚焦于视觉-语言交互——即语言与视觉模态融合以支持数据感知与理解。大语言模型的出现为通过自然语言与数据可视化进行交互引入了新范式，催生了直观、多模态且易于访问的界面。我们从六个维度分析了48篇论文：应用领域、可视化任务、可视化表示、交互模态、LLM集成方式以及系统评估。我们的分类框架描绘了LLM在整个可视化流程中的角色，涵盖从数据查询与转换到可视化生成、解释与导航。我们重点阐述了新兴的设计模式，指出了在可访问性与可视化解读方面存在的不足，并讨论了当前LLM在空间推理和上下文关联方面的局限性。此外，我们对结合LLM的可视化系统评估进行了反思，重点分析了当前研究如何应对这一挑战，并探讨了对此类系统进行有效评估时存在的现有空白。通过本次综述，我们旨在为LLM增强的可视化领域的未来研究与系统设计提供指引，以支持广泛的用户群体并推动智能对话式界面的发展。