Summary of From Bytes to Borsch: Fine-tuning Gemma and Mistral For the Ukrainian Language Representation, by Artur Kiulian et al.
From Bytes to Borsch: Fine-Tuning Gemma and Mistral for the Ukrainian Language Representation
by Artur Kiulian, Anton Polishko, Mykola Khandoga, Oryna Chubych, Jack Connor, Raghav Ravishankar, Adarsh Shirawalmath
First submitted to arxiv on: 14 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper focuses on addressing the limitation of large language models (LLMs) in processing low-resource languages like Ukrainian, which hinders their adoption and relevance. To improve the linguistic proficiency of Gemma and Mistral LLMs, the authors fine-tune these models with Ukrainian datasets and benchmark them against existing models capable of processing Ukrainian. This effort promotes inclusivity in the digital realm and mitigates language bias in technology. The paper also presents the Ukrainian Knowledge and Instruction Dataset (UKID) to aid future efforts in language model fine-tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making big language models work better with languages like Ukrainian, which are less common but still important. Right now, these models aren’t very good at understanding and generating text in Ukrainian, which limits their usefulness. The authors want to change this by adjusting the models to work better with Ukrainian data. They also created a new dataset that will help others make similar improvements. |
Keywords
» Artificial intelligence » Fine tuning » Language model
