Gemv2: Multilingual nlg benchmarking in a single line of code
arXiv preprint arXiv:2206.11249, 2022•arxiv.org
Evaluation in machine learning is usually informed by past choices, for example which
datasets or metrics to use. This standardization enables the comparison on equal footing
using leaderboards, but the evaluation choices become sub-optimal as better alternatives
arise. This problem is especially pertinent in natural language generation which requires
ever-improving suites of datasets, metrics, and human evaluation to make definitive claims.
To make following best model evaluation practices easier, we introduce GEMv2. The new …
datasets or metrics to use. This standardization enables the comparison on equal footing
using leaderboards, but the evaluation choices become sub-optimal as better alternatives
arise. This problem is especially pertinent in natural language generation which requires
ever-improving suites of datasets, metrics, and human evaluation to make definitive claims.
To make following best model evaluation practices easier, we introduce GEMv2. The new …
Evaluation in machine learning is usually informed by past choices, for example which datasets or metrics to use. This standardization enables the comparison on equal footing using leaderboards, but the evaluation choices become sub-optimal as better alternatives arise. This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims. To make following best model evaluation practices easier, we introduce GEMv2. The new version of the Generation, Evaluation, and Metrics Benchmark introduces a modular infrastructure for dataset, model, and metric developers to benefit from each others work. GEMv2 supports 40 documented datasets in 51 languages. Models for all datasets can be evaluated online and our interactive data card creation and rendering tools make it easier to add new datasets to the living benchmark.
arxiv.org