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 at http://square.ukp-lab.de.

翻译：问答数据集的持续发展引起了研究界对多领域模型的关注。一种流行的方法是使用多数据集模型，即在多个数据集上训练的模型，以学习其规律并防止对单个数据集的过拟合。然而，随着GitHub或Hugging Face等在线存储库中问答模型的激增，另一种替代方案正变得可行。最近的研究表明，结合专家智能体可以在性能上显著超越多数据集模型。为了简化多智能体模型的研究，我们扩展了UKP-SQuARE（一个面向问答研究的在线平台），以支持三类多智能体系统：i）智能体选择，ii）智能体早期融合，以及iii）智能体后期融合。我们进行实验评估其推理速度，并讨论与多数据集模型相比的性能与速度权衡。UKP-SQuARE是开源的，并可通过http://square.ukp-lab.de公开获取。