[A Diversity Diet for a Healthier Model: A Case Study of French ModernBERT](https://aclanthology.org/2026.findings-acl.1707.pdf)
[Louis Estève](/people/louis-esteve/),
[Christophe Servan](/people/christophe-servan-7075/),
[Thomas Lavergne](/people/thomas-lavergne/unverified/),
[Agata Savary](/people/agata-savary/)
Abstract
Diversity has been gaining interest in the NLP community in recent years. At the same time, state-of-the-art transformer models such as ModernBERT use very large pre-training datasets, which are driven by size rather than by diversity. This summons to investigate theimpact of diversity on pre-training. We do so in this study, with the express intent of reducing pre-training dataset size, while retaining atleast comparable performance. We compare diversity-driven sampling algorithms, and we use the best one to pre-train several ModernBERT models on French with a fixed compute budget. We fine-tune and evaluate them on a variety of French benchmarks. We compare them with models pre-trained on randomly sampled data of commensurate size, with the same compute budget. We find that both random and diversity-driven sampling may reduce the pre-training dataset by up to 94% and the pre-training time by up to 73% while maintaining performance. Moreover, in some tasks, the inherent quality of models, estimated via head-only fine-tuning, is up to 10 points higher with diversity sampling than with random sampling.- Anthology ID:
- 2026.findings-acl.1707
- Volume:
[Findings of the Association for Computational Linguistics: ACL 2026](/volumes/2026.findings-acl/)- Month:
- July
- Year:
- 2026
- Address:
- San Diego, California, United States
- Editors:
[Maria Liakata](/people/maria-liakata/),[Viviane P. Moreira](/people/viviane-p-moreira/unverified/),[Jiajun Zhang](/people/jiajun-zhang/unverified/),[David Jurgens](/people/david-jurgens/)- Venue:
[Findings](/venues/findings/)- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 34168–34181
- Language:
- URL:
[https://aclanthology.org/2026.findings-acl.1707/](https://aclanthology.org/2026.findings-acl.1707/)- DOI:
- Cite (ACL):
- Louis Estève, Christophe Servan, Thomas Lavergne, and Agata Savary. 2026. A Diversity Diet for a Healthier Model: A Case Study of French ModernBERT. InFindings of the Association for Computational Linguistics: ACL 2026, pages 34168–34181, San Diego, California, United States. Association for Computational Linguistics. - Cite (Informal):
[A Diversity Diet for a Healthier Model: A Case Study of French ModernBERT](https://aclanthology.org/2026.findings-acl.1707/)(Estève et al., Findings 2026)- PDF:
[https://aclanthology.org/2026.findings-acl.1707.pdf](https://aclanthology.org/2026.findings-acl.1707.pdf)