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BgGPT 1.0: Extending English-centric LLMs to other languages

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arxiv 2412.10893 v1 pith:HE4YNDUP submitted 2024-12-14 cs.CL cs.AIcs.LG

classification cs.CLcs.AIcs.LG
keywords modelscapabilitiesbulgarianlanguagedatagemma-2demonstrateenglish
verification ladder T0 review T1 audit T2 compute T3 formal
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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 3 Pith papers

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  1. Making Open-Source Text LLM Watermarks Durable Against Merging

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    A new training method, Merge-Adversarial Training, makes watermarks embedded in open-source LLMs survive model merging, boosting post-merge detection by up to 51 percentage points.

  2. Beyond N-Grams: Rethinking Evaluation Metrics and Strategies for Multilingual Abstractive Summarization

    cs.CL 2025-07 conditional novelty 6.0 of 10

    Across eight languages, n-gram metrics such as ROUGE correlate less with human ratings in fusional languages than in isolating and agglutinative ones, while the neural metric COMET correlates better, especially in low...

  3. Teaching a Language Model to Speak the Language of Tools

    cs.IR 2025-06 conditional novelty 5.0 of 10

    LoRA fine-tuning of BgGPT models on a bilingual Bulgarian function-calling dataset yields large gains on a self-built 120-case benchmark while keeping knowledge benchmarks stable.

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