Human-AI collaborative tools attract attentions from the data storytelling community to lower the barrier of expertise and streamline the workflow. The recent advance in large-scale generative AI techniques, e.g., large language models (LLMs) and text-to-image models, has the potential to enhance data storytelling with their power in visual and narration generation. After two years since these techniques were publicly available, it is important to reflect our progress of applying them and have an outlook for future opportunities. To achieve the goal, we compare the collaboration patterns of the latest tools with those of earlier ones using a dedicated framework for understanding human-AI collaboration in data storytelling. Through comparison, we identify persistent collaboration patterns, e.g., human-creator + AI-assistant, and emerging ones, e.g., AI-creator + human-reviewer. The benefits of these AI techniques and other implications to human-AI collaboration are also revealed. We further propose future directions to hopefully ignite innovations.

翻译：人机协作工具因其能够降低专业门槛并优化工作流程而受到数据叙事领域的关注。近期大规模生成式人工智能技术（例如大型语言模型和文生图模型）凭借其在视觉与叙事生成方面的强大能力，为数据叙事的增强提供了可能。自这些技术公开应用两年以来，有必要反思其应用进展并展望未来机遇。为此，我们采用专门用于理解数据叙事中人机协作的框架，将最新工具与早期工具的协作模式进行比较。通过对比，我们识别出持续存在的协作模式（如“人类创作者+AI助手”）与新兴模式（如“AI创作者+人类评审者”），同时揭示了这些AI技术带来的优势及其对人机协作的其他启示。我们进一步提出未来研究方向，以期激发创新突破。