DUMB: A Benchmark for Smart Evaluation of Dutch Models

• URL: http://arxiv.org/abs/2305.13026v2
• Date: Fri, 13 Oct 2023 10:43:05 GMT
• Title: DUMB: A Benchmark for Smart Evaluation of Dutch Models
• Authors: Wietse de Vries, Martijn Wieling and Malvina Nissim
• Abstract summary: We introduce the Dutch Model Benchmark: DUMB. The benchmark includes a diverse set of datasets for low-, medium- and high-resource tasks. Relative Error Reduction (RER) compares the DUMB performance of language models to a strong baseline. Highest performance is achieved by DeBERTaV3 (large), XLM-R (large) and mDeBERTaV3 (base)
• Score: 23.811515104842826
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We introduce the Dutch Model Benchmark: DUMB. The benchmark includes a diverse set of datasets for low-, medium- and high-resource tasks. The total set of nine tasks includes four tasks that were previously not available in Dutch. Instead of relying on a mean score across tasks, we propose Relative Error Reduction (RER), which compares the DUMB performance of language models to a strong baseline which can be referred to in the future even when assessing different sets of language models. Through a comparison of 14 pre-trained language models (mono- and multi-lingual, of varying sizes), we assess the internal consistency of the benchmark tasks, as well as the factors that likely enable high performance. Our results indicate that current Dutch monolingual models under-perform and suggest training larger Dutch models with other architectures and pre-training objectives. At present, the highest performance is achieved by DeBERTaV3 (large), XLM-R (large) and mDeBERTaV3 (base). In addition to highlighting best strategies for training larger Dutch models, DUMB will foster further research on Dutch. A public leaderboard is available at https://dumbench.nl.

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