IDAT: A Multi-Modal Dataset and Toolkit for Building and Evaluating Interactive Task-Solving Agents
<|reference_start|>IDAT: A Multi-Modal Dataset and Toolkit for Building and Evaluating Interactive Task-Solving Agents: Seamless interaction between AI agents and humans using natural language remains a key goal in AI research. This paper addresses the challenges of developing interactive agents capable of understanding and executing grounded natural language instructions through the IGLU competition at NeurIPS. Despite advancements, challenges such as a scarcity of appropriate datasets and the need for effective evaluation platforms persist. We introduce a scalable data collection tool for gathering interactive grounded language instructions within a Minecraft-like environment, resulting in a Multi-Modal dataset with around 9,000 utterances and over 1,000 clarification questions. Additionally, we present a Human-in-the-Loop interactive evaluation platform for qualitative analysis and comparison of agent performance through multi-turn communication with human annotators. We offer to the community these assets referred to as IDAT (IGLU Dataset And Toolkit) which aim to advance the development of intelligent, interactive AI agents and provide essential resources for further research.<|reference_end|>
@article{mohanty2024idat:,
    title={IDAT: A Multi-Modal Dataset and Toolkit for Building and Evaluating
  Interactive Task-Solving Agents},
    author={Shrestha Mohanty, Negar Arabzadeh, Andrea Tupini, Yuxuan Sun, Alexey
  Skrynnik, Artem Zholus, Marc-Alexandre C^ot'e, Julia Kiseleva},
    journal={arXiv preprint arXiv:2407.08898},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.08898},
    primaryClass={cs.AI cs.CL cs.LG}
}