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We introduce Dynaboard, an evaluation-as-a-service framework for hostingbenchmarks and conducting holistic model comparison, integrated with theDynabench platform. Our platform evaluates NLP models directly instead ofrelying on self-reported metrics or predictions on a single dataset. Under thisparadigm, models are submitted to be evaluated in the cloud, circumventing theissues of reproducibility, accessibility, and backwards compatibility thatoften hinder benchmarking in NLP. This allows users to interact with uploadedmodels in real time to assess their quality, and permits the collection ofadditional metrics such as memory use, throughput, and robustness, which --despite their importance to practitioners -- have traditionally been absentfrom leaderboards. On each task, models are ranked according to the Dynascore,a novel utility-based aggregation of these statistics, which users cancustomize to better reflect their preferences, placing more/less weight on aparticular axis of evaluation or dataset. As state-of-the-art NLP models pushthe limits of traditional benchmarks, Dynaboard offers a standardized solutionfor a more diverse and comprehensive evaluation of model quality.

Dynaboard: An Evaluation-As-A-Service Platform for Holistic Next-Generation Benchmarking