As a part of an embodied agent, Large Language Models (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 ambiguity detection have been proposed. However, it is difficult to compare them because they are tested on different datasets and there is no universal benchmark. For this reason, 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 1000 pairs of ambiguous tasks and their unambiguous counterparts, categorized by ambiguity type (Human Preferences, Common Sense Knowledge, Safety), with environment descriptions, clarifying questions and answers, user intents, and task plans, for a total of 2000 tasks. We hope that AmbiK will enable researchers to perform a unified comparison of ambiguity detection methods.
DOI: 10.18653/v1/2025.acl-long.1593
Скачать статью (PDF) из архива конференции ACL (англ.): https://aclanthology.org/2025.acl-long.1593/
Скачать статью (PDF) на arXiv.org (англ.): https://arxiv.org/abs/2506.04089
Скачать датасет на GitHub: https://github.com/cog-model/AmbiK-dataset
ResearchGate: https://www.researchgate.net/publication/392405795_AmbiK_Dataset_of_Ambiguous_Tasks_in_Kitchen_Environment
Anastasia Ivanova, Bakaeva Eva, Zoya Volovikova, Alexey Kovalev, and Aleksandr Panov. 2025. AmbiK: Dataset of Ambiguous Tasks in Kitchen Environment // In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 33216–33241, Vienna, Austria.