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DataDivers aims to enhance natural language processing by developing a framework to measure data diversity in datasets. The project will explore how diversity affects model performance and create methods to improve fairness and robustness in NLP applications.
Despite great progress in the field of Natural Language Processing (NLP), the field is still struggling to ensure the robustness and fairness of models.
So far, NLP has prioritized data size over data quality.
Yet there is growing evidence suggesting that the diversity of data, a key dimension of data quality, is crucial for fair and robust NLP models.
Many researchers are therefore trying to create more diverse datasets, but there is no clear path for them to follow.
Even the fundamental questi…
UNIVERSITEIT UTRECHT
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