People are relying on AI agents to assist them with various tasks. The human must know when to rely on the agent, collaborate with the agent, or ignore its suggestions. In this work, we propose to learn rules, grounded in data regions and described in natural language, that illustrate how the human should collaborate with the AI. Our novel region discovery algorithm finds local regions in the data as neighborhoods in an embedding space where prior human behavior should be corrected. Each region is then described using a large language model in an iterative and contrastive procedure. We then teach these rules to the human via an onboarding stage. Through user studies on object detection and question-answering tasks, we show that our method can lead to more accurate human-AI teams. We also evaluate our region discovery and description algorithms separately.

翻译：人们越来越依赖AI代理来协助完成各种任务。人类必须知道何时依赖代理、何时与代理协作或忽略其建议。本文提出一种学习方法，通过数据区域驱动的自然语言规则，阐明人类应如何与AI协作。我们的新型区域发现算法在嵌入空间中找到数据中的局部区域（即邻域），并识别这些区域中先前人类行为需要纠正的部分。随后通过迭代对比过程，利用大语言模型对每个区域进行描述。最后，我们通过引导阶段将这些规则传授给人类。通过目标检测与问答任务的用户研究，我们证明该方法可构建更精准的人机协作团队。此外，我们还分别评估了区域发现与描述算法的性能。