BgGPT 1.0: Extending English-centric LLMs to other languages
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Abstract (arXiv)
We present BgGPT-Gemma-2-27B-Instruct and BgGPT-Gemma-2-9B-Instruct: continually pretrained and fine-tuned versions of Google's Gemma-2 models, specifically optimized for Bulgarian language understanding and generation. Leveraging Gemma-2's multilingual capabilities and over 100 billion tokens of Bulgarian and English text data, our models demonstrate strong performance in Bulgarian language tasks, setting a new standard for language-specific AI models. Our approach maintains the robust capabilities of the original Gemma-2 models, ensuring that the English language performance remains intact. To preserve the base model capabilities, we incorporate continual learning strategies based on recent Branch-and-Merge techniques as well as thorough curation and selection of training data. We provide detailed insights into our methodology, including the release of model weights with a commercial-friendly license, enabling broader adoption by researchers, companies, and hobbyists. Further, we establish a comprehensive set of benchmarks based on non-public educational data sources to evaluate models on Bulgarian language tasks as well as safety and chat capabilities. Our findings demonstrate the effectiveness of fine-tuning state-of-the-art models like Gemma 2 to enhance language-specific AI applications while maintaining cross-lingual capabilities.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 26 of its 60 references that are in the map. First posted on arXiv 14 Dec 2024.
Builds on
26 of 60 references.
References · 26
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14 more
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Built on by
Citing papers per year: 2025: 4; 2026: 4.
8 papers.
- 2025Krikri: Advancing Open Large Language Models for Greekcited by 14
- 2025In-context Learning vs. Instruction Tuning: The Case of Small and Multilingual Language Modelscited by 3
- 2025Beyond N-Grams: Rethinking Evaluation Metrics and Strategies for Multilingual Abstractive Summarizationcited by 3
- 2025Teaching a Language Model to Speak the Language of Toolscited by 0
- 2026The Hrunting of AI: Where and How to Improve English Dialectal Fairnesscited by 0
- 2026Jupiter-N Technical Reportcited by 0
- 2026Sch"utzen: Evaluating LLM Safety in Bulgarian and German Contextscited by 0
- 2026Does Machine Translation Retain Media Bias?cited by 0
- 2026Does Machine Translation Retain Media Bias?14 Oct 2026
- 2026Sch"utzen: Evaluating LLM Safety in Bulgarian and German Contexts9 Jun 2026
- 2026Jupiter-N Technical Report19 Apr 2026
- 2026The Hrunting of AI: Where and How to Improve English Dialectal Fairness16 Mar 2026
- 2025Beyond N-Grams: Rethinking Evaluation Metrics and Strategies for Multilingual Abstractive Summarization11 Jul 2025
- 2025Teaching a Language Model to Speak the Language of Tools29 Jun 2025
- 2025Krikri: Advancing Open Large Language Models for Greek19 May 2025
- 2025In-context Learning vs. Instruction Tuning: The Case of Small and Multilingual Language Models3 Mar 2025