IDAT: A Multi-Modal Dataset and Toolkit for Building and Evaluating Interactive Task-Solving Agents.

Shrestha Mohanty|Negar Arabzadeh|Andrea Tupini|Yuxuan Sun|Alexey Skrynnik|Artem Zholus|Marc-Alexandre Côté|Julia Kiseleva


Anthology ID:DBLP:conf/sigir/MohantyAT0SZCK25
Volume:Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2025, Padua, Italy, July 13-18, 2025
Year:2025
Venue:Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)
Publisher:ACM
Pages:3551-3562
URL:https://doi.org/10.1145/3726302.3730300
DOI:https://doi.org/10.1145/3726302.3730300
DBLP:conf/sigir/MohantyAT0SZCK25
BibTeX:
@inproceedings{mohanty-2025-idat, author = {Shrestha Mohanty and Negar Arabzadeh and Andrea Tupini and Yuxuan Sun and Alexey Skrynnik and Artem Zholus and Marc-Alexandre C\^{o}t\'{e} and Julia Kiseleva}, editor = {Nicola Ferro and Maria Maistro and Gabriella Pasi and Omar Alonso and Andrew Trotman and Suzan Verberne}, title = {{IDAT: A Multi-Modal Dataset and Toolkit for Building and Evaluating Interactive Task-Solving Agents}}, booktitle = {{Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2025, Padua, Italy, July 13-18, 2025}}, pages = {3551--3562}, publisher = {ACM}, year = {2025}, url = {https://doi.org/10.1145/3726302.3730300}, doi = {https://doi.org/10.1145/3726302.3730300} }