In developing machine learning (ML) models for text classification, one common challenge is that the collected data is often not ideally distributed, especially when new classes are introduced in response to changes of data and tasks. In this paper, we present a solution for using visual analytics (VA) to guide the generation of synthetic data using large language models. As VA enables model developers to identify data-related deficiency, data synthesis can be targeted to address such deficiency. We discuss different types of data deficiency, describe different VA techniques for supporting their identification, and demonstrate the effectiveness of targeted data synthesis in improving model accuracy. In addition, we present a software tool, iGAiVA, which maps four groups of ML tasks into four VA views, integrating generative AI and VA into an ML workflow for developing and improving text classification models.

翻译：在开发用于文本分类的机器学习模型时，一个常见的挑战是所收集的数据通常并非理想分布，尤其是在数据和任务发生变化而引入新类别的情况下。本文提出一种解决方案，利用可视分析来指导基于大语言模型的合成数据生成。由于可视分析使模型开发者能够识别与数据相关的缺陷，数据合成便可针对性地弥补这些缺陷。我们讨论了不同类型的数据缺陷，描述了支持其识别的不同可视分析技术，并证明了针对性数据合成在提升模型准确性方面的有效性。此外，我们介绍了一款软件工具 iGAiVA，该工具将四组机器学习任务映射到四个可视分析视图中，从而将生成式人工智能与可视分析集成到一个用于开发和改进文本分类模型的机器学习工作流中。