The field of data analysis and visualization is rapidly evolving, with a focus on developing innovative methods to facilitate the interpretation and understanding of complex data. Recent research has concentrated on leveraging large language models (LLMs) to enhance data analysis, visualization, and user interaction. Notably, the integration of LLMs with visual dataflow programming models has shown promise in supporting analysts in authoring, debugging, and refining data analyses. Furthermore, the development of multimodal prompting techniques has improved the usability of LLMs for visualization authoring, enabling users to create visualizations more intuitively. Additionally, research has explored the use of LLMs in semantic data processing, facilitating the analysis of unstructured text data. Overall, these advancements aim to democratize data science, enhance user experience, and foster more effective human-AI collaboration. Noteworthy papers include ChartQA-X, which presents a comprehensive dataset for generating explanations alongside answering questions about chart images, and VisPilot, which introduces a multimodal prompting approach for visualization authoring. Flowco is also notable, as it leverages a visual dataflow programming model to support data analysis. VizTA and SteerWrangler are other examples of innovative systems that enhance comprehension of distributional visualization and support semantic data processing, respectively.