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arxiv: 2412.10893 · v1 · pith:HE4YNDUPnew · submitted 2024-12-14 · 💻 cs.CL · cs.AI· cs.LG

BgGPT 1.0: Extending English-centric LLMs to other languages

classification 💻 cs.CL cs.AIcs.LG
keywords modelscapabilitiesbulgarianlanguagedatagemma-2demonstrateenglish
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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.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Sch\"utzen: Evaluating LLM Safety in Bulgarian and German Contexts

    cs.CL 2026-06 unverdicted novelty 6.0

    Schützen is a German-Bulgarian LLM safety dataset showing pronounced cross-language differences in model safety behavior.