The development of state-of-the-art systems in different applied areas of machine learning (ML) is driven by benchmarks, which have shaped the paradigm of evaluating generalisation capabilities from multiple perspectives. Although the paradigm is shifting towards more fine-grained evaluation across diverse tasks, the delicate question of how to aggregate the performances has received particular interest in the community. In general, benchmarks follow the unspoken utilitarian principles, where the systems are ranked based on their mean average score over task-specific metrics. Such aggregation procedure has been viewed as a sub-optimal evaluation protocol, which may have created the illusion of progress. This paper proposes Vote'n'Rank, a framework for ranking systems in multi-task benchmarks under the principles of the social choice theory. We demonstrate that our approach can be efficiently utilised to draw new insights on benchmarking in several ML sub-fields and identify the best-performing systems in research and development case studies. The Vote'n'Rank's procedures are more robust than the mean average while being able to handle missing performance scores and determine conditions under which the system becomes the winner.

翻译：在机器学习（ML）的不同应用领域，先进系统的发展由基准测试驱动，这些测试已从多角度塑造了泛化能力的评估范式。尽管该范式正转向跨多样任务的更细粒度评估，如何聚合性能这一微妙问题已在学界引发特别关注。总体而言，基准测试遵循未言明的效用主义原则，即根据系统在任务特定指标上的平均得分进行排名。这种聚合流程被视为次优的评估协议，可能制造了进步的假象。本文提出Vote'n'Rank框架，基于社会选择理论原则实现多任务基准测试中的系统排名。我们证明，该方法可有效用于从ML多个子领域的基准测试中挖掘新见解，并在研发案例中识别性能最优系统。Vote'n'Rank的流程比均值平均更稳健，同时能处理缺失性能分数，并确定系统成为优胜者的条件。