@inproceedings{puerto-etal-2023-ukp,
title = "{UKP}-{SQ}u{ARE} v3: A Platform for Multi-Agent {QA} Research",
author = {Puerto, Haritz and
Baumg{\"a}rtner, Tim and
Sachdeva, Rachneet and
Fang, Haishuo and
Zhang, Hao and
Tariverdian, Sewin and
Wang, Kexin and
Gurevych, Iryna},
editor = "Bollegala, Danushka and
Huang, Ruihong and
Ritter, Alan",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-demo.55",
doi = "10.18653/v1/2023.acl-demo.55",
pages = "569--580",
abstract = "The continuous development of Question Answering (QA) datasets has drawn the research community{'}s attention toward multi-domain models. A popular approach is to use multi-dataset models, which are models trained on multiple datasets to learn their regularities and prevent overfitting to a single dataset. However, with the proliferation of QA models in online repositories such as GitHub or Hugging Face, an alternative is becoming viable. Recent works have demonstrated that combining expert agents can yield large performance gains over multi-dataset models. To ease research in multi-agent models, we extend UKP-SQuARE, an online platform for QA research, to support three families of multi-agent systems: i) agent selection, ii) early-fusion of agents, and iii) late-fusion of agents. We conduct experiments to evaluate their inference speed and discuss the performance vs. speed trade-off compared to multi-dataset models. UKP-SQuARE is open-source and publicly available.",
}
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<abstract>The continuous development of Question Answering (QA) datasets has drawn the research community’s attention toward multi-domain models. A popular approach is to use multi-dataset models, which are models trained on multiple datasets to learn their regularities and prevent overfitting to a single dataset. However, with the proliferation of QA models in online repositories such as GitHub or Hugging Face, an alternative is becoming viable. Recent works have demonstrated that combining expert agents can yield large performance gains over multi-dataset models. To ease research in multi-agent models, we extend UKP-SQuARE, an online platform for QA research, to support three families of multi-agent systems: i) agent selection, ii) early-fusion of agents, and iii) late-fusion of agents. We conduct experiments to evaluate their inference speed and discuss the performance vs. speed trade-off compared to multi-dataset models. UKP-SQuARE is open-source and publicly available.</abstract>
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%0 Conference Proceedings
%T UKP-SQuARE v3: A Platform for Multi-Agent QA Research
%A Puerto, Haritz
%A Baumgärtner, Tim
%A Sachdeva, Rachneet
%A Fang, Haishuo
%A Zhang, Hao
%A Tariverdian, Sewin
%A Wang, Kexin
%A Gurevych, Iryna
%Y Bollegala, Danushka
%Y Huang, Ruihong
%Y Ritter, Alan
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F puerto-etal-2023-ukp
%X The continuous development of Question Answering (QA) datasets has drawn the research community’s attention toward multi-domain models. A popular approach is to use multi-dataset models, which are models trained on multiple datasets to learn their regularities and prevent overfitting to a single dataset. However, with the proliferation of QA models in online repositories such as GitHub or Hugging Face, an alternative is becoming viable. Recent works have demonstrated that combining expert agents can yield large performance gains over multi-dataset models. To ease research in multi-agent models, we extend UKP-SQuARE, an online platform for QA research, to support three families of multi-agent systems: i) agent selection, ii) early-fusion of agents, and iii) late-fusion of agents. We conduct experiments to evaluate their inference speed and discuss the performance vs. speed trade-off compared to multi-dataset models. UKP-SQuARE is open-source and publicly available.
%R 10.18653/v1/2023.acl-demo.55
%U https://aclanthology.org/2023.acl-demo.55
%U https://doi.org/10.18653/v1/2023.acl-demo.55
%P 569-580
Markdown (Informal)
[UKP-SQuARE v3: A Platform for Multi-Agent QA Research](https://aclanthology.org/2023.acl-demo.55) (Puerto et al., ACL 2023)
ACL
- Haritz Puerto, Tim Baumgärtner, Rachneet Sachdeva, Haishuo Fang, Hao Zhang, Sewin Tariverdian, Kexin Wang, and Iryna Gurevych. 2023. UKP-SQuARE v3: A Platform for Multi-Agent QA Research. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 569–580, Toronto, Canada. Association for Computational Linguistics.