@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.",
}

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.
Puerto, Haritz Baumg{\"a}rtner, Tim Sachdeva, Rachneet Fang, Haishuo Zhang, Hao Tariverdian, Sewin Wang, Kexin Gurevych, Iryna
UKP-SQuARE v3: A Platform for Multi-Agent QA Research