@inproceedings{hasegawa-etal-2026-promqa,
title = "{P}ro{MQA}-Assembly: Multimodal Procedural {QA} Dataset on Assembly",
author = "Hasegawa, Kimihiro and
Imrattanatrai, Wiradee and
Asada, Masaki and
Holm, Susan E. and
Wang, Yuran and
Zhou, Xuanang and
Fukuda, Ken and
Mitamura, Teruko",
editor = "Piperidis, Stelios and
Bel, N{\'u}ria and
van den Heuvel, Henk and
Ide, Nancy and
Krek, Simon and
Toral, Antonio",
booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resource Association",
url = "https://aclanthology.org/2026.lrec-1.714/",
doi = "10.63317/2cbr6347bzzu",
pages = "9082--9104",
abstract = "Assistants on assembly tasks show great potential to benefit humans ranging from helping with everyday tasks to interacting in industrial settings. However, evaluation resources in assembly activities are underexplored. To foster system development, we propose a new multimodal QA evaluation dataset on assembly activities. Our dataset, ProMQA-Assembly, consists of 646 QA pairs that require multimodal understanding of human activity videos and their instruction manuals in an online-style manner. For cost effectiveness in the data creation, we adopt a semi-automated QA annotation approach, where LLMs generate candidate QA pairs and humans verify them. We further improve QA generation by integrating fine-grained action labels to diversify question types. Additionally, we create 81 instruction task graphs for our target assembly tasks. These newly created task graphs are used in our benchmarking experiment, as well as in facilitating the human verification process. With our dataset, we benchmark models, including competitive proprietary multimodal models. We find that ProMQA-Assembly contains challenging multimodal questions, where reasoning models showcase promising results. We believe our new evaluation dataset contributes to the further development of procedural-activity assistants."
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<abstract>Assistants on assembly tasks show great potential to benefit humans ranging from helping with everyday tasks to interacting in industrial settings. However, evaluation resources in assembly activities are underexplored. To foster system development, we propose a new multimodal QA evaluation dataset on assembly activities. Our dataset, ProMQA-Assembly, consists of 646 QA pairs that require multimodal understanding of human activity videos and their instruction manuals in an online-style manner. For cost effectiveness in the data creation, we adopt a semi-automated QA annotation approach, where LLMs generate candidate QA pairs and humans verify them. We further improve QA generation by integrating fine-grained action labels to diversify question types. Additionally, we create 81 instruction task graphs for our target assembly tasks. These newly created task graphs are used in our benchmarking experiment, as well as in facilitating the human verification process. With our dataset, we benchmark models, including competitive proprietary multimodal models. We find that ProMQA-Assembly contains challenging multimodal questions, where reasoning models showcase promising results. We believe our new evaluation dataset contributes to the further development of procedural-activity assistants.</abstract>
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%0 Conference Proceedings
%T ProMQA-Assembly: Multimodal Procedural QA Dataset on Assembly
%A Hasegawa, Kimihiro
%A Imrattanatrai, Wiradee
%A Asada, Masaki
%A Holm, Susan E.
%A Wang, Yuran
%A Zhou, Xuanang
%A Fukuda, Ken
%A Mitamura, Teruko
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F hasegawa-etal-2026-promqa
%X Assistants on assembly tasks show great potential to benefit humans ranging from helping with everyday tasks to interacting in industrial settings. However, evaluation resources in assembly activities are underexplored. To foster system development, we propose a new multimodal QA evaluation dataset on assembly activities. Our dataset, ProMQA-Assembly, consists of 646 QA pairs that require multimodal understanding of human activity videos and their instruction manuals in an online-style manner. For cost effectiveness in the data creation, we adopt a semi-automated QA annotation approach, where LLMs generate candidate QA pairs and humans verify them. We further improve QA generation by integrating fine-grained action labels to diversify question types. Additionally, we create 81 instruction task graphs for our target assembly tasks. These newly created task graphs are used in our benchmarking experiment, as well as in facilitating the human verification process. With our dataset, we benchmark models, including competitive proprietary multimodal models. We find that ProMQA-Assembly contains challenging multimodal questions, where reasoning models showcase promising results. We believe our new evaluation dataset contributes to the further development of procedural-activity assistants.
%R 10.63317/2cbr6347bzzu
%U https://aclanthology.org/2026.lrec-1.714/
%U https://doi.org/10.63317/2cbr6347bzzu
%P 9082-9104
Markdown (Informal)
[ProMQA-Assembly: Multimodal Procedural QA Dataset on Assembly](https://aclanthology.org/2026.lrec-1.714/) (Hasegawa et al., LREC 2026)
ACL
- Kimihiro Hasegawa, Wiradee Imrattanatrai, Masaki Asada, Susan E. Holm, Yuran Wang, Xuanang Zhou, Ken Fukuda, and Teruko Mitamura. 2026. ProMQA-Assembly: Multimodal Procedural QA Dataset on Assembly. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 9082–9104, Palma de Mallorca, Spain. ELRA Language Resource Association.