Despite the ubiquity of visualization examples published on the web, retargeting existing custom chart implementations to new datasets remains difficult, time-intensive, and tedious. The adaptation process assumes author familiarity with both the implementation of the example as well as how the new dataset might need to be transformed to fit into the example code. With recent advances in Large Language Models (LLMs), automatic adaptation of code can be achieved from high-level user prompts, reducing the barrier for visualization retargeting. To better understand how LLMs can assist retargeting and its potential limitations, we characterize and evaluate the performance of LLM assistance across multiple datasets and charts of varying complexity, categorizing failures according to type and severity. In our evaluation, we compare two approaches: (1) directly instructing the LLM model to fully generate and adapt code by treating code as text inputs and (2) a more constrained program synthesis pipeline where the LLM guides the code construction process by providing structural information (e.g., visual encodings) based on properties of the example code and data. We find that both approaches struggle when new data has not been appropriately transformed, and discuss important design recommendations for future retargeting systems.

翻译：尽管网络上发布的可视化示例无处不在，但将现有的自定义图表实现重定向至新数据集仍然困难、耗时且繁琐。适配过程要求作者既熟悉示例的实现方式，又了解新数据集需要如何转换才能适配示例代码。随着大语言模型（LLMs）的最新进展，现在可以通过高级用户提示自动实现代码适配，从而降低可视化重定向的门槛。为了更好地理解大语言模型如何辅助重定向及其潜在局限性，我们对大语言模型在不同数据集和复杂度各异的图表上的辅助性能进行了系统性表征与评估，并依据错误类型和严重程度对失败案例进行分类。在评估中，我们比较了两种方法：（1）通过将代码视为文本输入，直接指导大语言模型完整生成和适配代码；（2）采用约束性更强的程序合成流程，其中大语言模型根据示例代码和数据的特性提供结构信息（如视觉编码）来引导代码构建过程。我们发现，当新数据未经适当转换时，两种方法均面临困难，并针对未来重定向系统提出了重要的设计建议。