We Need to Measure Data Diversity in NLP -- Better and Broader
Fuente:
arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866916958655479808 |
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| author | Nguyen, Dong Ploeger, Esther |
| author_facet | Nguyen, Dong Ploeger, Esther |
| contents | Although diversity in NLP datasets has received growing attention, the question of how to measure it remains largely underexplored. This opinion paper examines the conceptual and methodological challenges of measuring data diversity and argues that interdisciplinary perspectives are essential for developing more fine-grained and valid measures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_20264 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | We Need to Measure Data Diversity in NLP -- Better and Broader Nguyen, Dong Ploeger, Esther Computation and Language Artificial Intelligence Although diversity in NLP datasets has received growing attention, the question of how to measure it remains largely underexplored. This opinion paper examines the conceptual and methodological challenges of measuring data diversity and argues that interdisciplinary perspectives are essential for developing more fine-grained and valid measures. |
| title | We Need to Measure Data Diversity in NLP -- Better and Broader |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2505.20264 |