mlsanityData to 14 Oct 2026

BgGPT 1.0: Extending English-centric LLMs to other languages

arXiv 2412.10893 ↗First posted 14 Dec 2024cs.CL cs.AI cs.LGcited by 8

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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

  1. 2024The Llama 3 Herd of Modelscited by 14,715
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  3. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  4. 2021Training Verifiers to Solve Math Word Problemscited by 10,109
  5. 2018Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challengecited by 4,886
  6. 2023Mistral 7Bcited by 3,164
  7. 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,218
  8. 2024Qwen2 Technical Reportcited by 2,120
  9. 2024Gemma 2: Improving Open Language Models at a Practical Sizecited by 1,998
  10. 2016Enriching Word Vectors with Subword Informationcited by 1,583
  11. 2019A Comprehensive Survey on Transfer Learningcited by 1,160
  12. 2013An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networkscited by 722
14 more
  1. 2023The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variantscited by 311
  2. 2024Continual Learning of Large Language Models: A Comprehensive Surveycited by 234
  3. 2024WildBench: Benchmarking LLMs with Challenging Tasks from Real Users in the Wildcited by 218
  4. 2024Arcee's MergeKit: A Toolkit for Merging Large Language Modelscited by 194
  5. 2024LLaMA Beyond English: An Empirical Study on Language Capability Transfercited by 95
  6. 2024Understanding and Mitigating Language Confusion in LLMscited by 75
  7. 2023Efficiently Adapting Pretrained Language Models To New Languagescited by 28
  8. 2024Adapting Multilingual LLMs to Low-Resource Languages using Continued Pre-training and Synthetic Corpuscited by 24
  9. 2024Scaling Laws for Multilingual Language Modelscited by 23
  10. 2023Fauno: The Italian Large Language Model that will leave you senza parole!cited by 13
  11. 2023VinaLLaMA: LLaMA-based Vietnamese Foundation Modelcited by 10
  12. 2023Continual Learning Under Language Shiftcited by 9
  13. 2024RedWhale: An Adapted Korean LLM Through Efficient Continual Pretrainingcited by 8
  14. 2024"Vorbecsti Romanecste?" A Recipe to Train Powerful Romanian LLMs with English Instructionscited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 4.

8 papers.