Fine Tuning Methods for Low-resource Languages

Tim Bakkenes, Daniel Wang, Anton Johansson
10/5/2025
cs.CLcs.LG

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Abstract

The rise of Large Language Models has not been inclusive of all cultures. The models are mostly trained on English texts and culture which makes them underperform in other languages and cultural contexts. By developing a generalizable method for preparing culturally relevant datasets and post-training the Gemma 2 model, this project aimed to increase the performance of Gemma 2 for an underrepresented language and showcase how others can do the same to unlock the power of Generative AI in their country and preserve their cultural heritage.

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Cite this paper

@article{bakkenes2025fine,
  title  = {Fine Tuning Methods for Low-resource Languages},
  author = {Tim Bakkenes and Daniel Wang and Anton Johansson},
  year   = {2025},
  eprint = {2510.04139},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2510.04139v1}
}

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