Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.04139 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908577228128256 |
|---|---|
| author | Bakkenes, Tim Wang, Daniel Johansson, Anton |
| author_facet | Bakkenes, Tim Wang, Daniel Johansson, Anton |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_04139 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fine Tuning Methods for Low-resource Languages Bakkenes, Tim Wang, Daniel Johansson, Anton Computation and Language Machine Learning 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. |
| title | Fine Tuning Methods for Low-resource Languages |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2510.04139 |