Effective Human-AI Teams via Learned Natural Language Rules and Onboarding
NeurIPS(2023)
摘要
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 that corrects the
human prior. Each region is then described using an iterative and contrastive
procedure where a large language model describes the region. 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.
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关键词
learned natural language rules,onboarding
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