AmbiK: Dataset of Ambiguous Tasks in Kitchen Environment

Abstract

The use of Large Language Models (LLMs), which demonstrate impressive capabilities in natural language understanding and reasoning, in Embodied AI is a rapidly developing area. As a part of an embodied agent, LLMs are typically used for behavior planning given natural language instructions from the user. However, dealing with ambiguous instructions in real-world environments remains a challenge for LLMs. Various methods for task disambiguation have been proposed. However, it is difficult to compare them because they work with different data. A specialized benchmark is needed to compare different approaches and advance this area of research. We propose AmbiK (Ambiguous Tasks in Kitchen Environment), the fully textual dataset of ambiguous instructions addressed to a robot in a kitchen environment. AmbiK was collected with the assistance of LLMs and is human-validated. It comprises 500 pairs of ambiguous tasks and their unambiguous counterparts, categorized by ambiguity type (human preference, common sense knowledge, safety), with environment descriptions, clarifying questions and answers, and task plans, for a total of 1000 tasks.