Fine Tuning Methods for Low-resource Languages
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 23 references that are in the map. First posted on arXiv 5 Oct 2025.
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4 of 23 references.
References · 4
- 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
- 2023Tackling Bias in Pre-trained Language Models: Current Trends and Under-represented Societiescited by 11
- 2024Stars, Stripes, and Silicon: Unravelling the ChatGPT's All-American, Monochrome, Cis-centric Biascited by 4
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