Generative Large Language Models (LLMs) show potential in data analysis, yet
their full capabilities remain uncharted. Our work explores the capabilities of
LLMs for creating and refining visualizations via conversational interfaces. We
used an LLM to conduct a re-analysis of a prior Wizard-of-Oz study examining
the use of chatbots for conducting visual analysis. We surfaced the strengths
and weaknesses of LLM-driven analytic chatbots, finding that they fell short in
supporting progressive visualization refinements. From these findings, we
developed AI Threads, a multi-threaded analytic chatbot that enables analysts
to proactively manage conversational context and improve the efficacy of its
outputs. We evaluate its usability through a crowdsourced study (n=40) and
in-depth interviews with expert analysts (n=10). We further demonstrate the
capabilities of AI Threads on a dataset outside the LLM's training corpus. Our
findings show the potential of LLMs while also surfacing challenges and
fruitful avenues for future research.

我们的研究探索了生成式大型语言模型在通过对话界面创建和改进可视化方面的能力，发现生成式语言模型驱动的分析对话机器人在支持渐进式可视化改进方面存在不足。基于这些发现，我们开发了 AI Threads，一个多线程的分析对话机器人，能够主动管理对话上下文并提高其输出的效果。通过众包研究（n=40）和专家分析师的深入访谈（n=10），我们评估了其可用性。我们还在生成式语言模型的训练语料库之外的数据集上展示了 AI Threads 的能力，从而展示了生成式语言模型的潜力，同时也揭示了未来研究的挑战和有成果的方向。