Efficiently Adapting Large Language Models to Russian using Learned Embedding Propagation

Efficient Training of Russian Language Models through Learned Embedding Propagation

Adapting large language models (LLMs) to specific languages presents a challenge. A promising approach to improving the performance of LLMs in other languages, such as Russian, is called Learned Embedding Propagation (LEP). This method allows for the efficient adaptation of existing models trained in other languages, without requiring complete retraining.

The Challenges of Language-Specific Adaptation

Large language models are typically trained with massive datasets, often originating from English-language sources. Directly applying these models to other languages frequently leads to suboptimal results, as language-specific features, such as grammar, syntax, and vocabulary, are not adequately considered. However, complete retraining for each language is extremely resource-intensive and time-consuming.

Learned Embedding Propagation as a Solution

LEP offers a more efficient path to language-specific adaptation. Instead of retraining the entire model, LEP focuses on adapting the so-called embeddings. Embeddings are vector representations of words that capture their semantic meaning. By adapting these embeddings to the target language, the model's performance can be significantly improved without repeating the entire training process.

How Learned Embedding Propagation Works

LEP uses the existing embeddings of the pre-trained model as a basis and gradually adapts them to the target language. The relationships between words in the target language are analyzed, and the embeddings are adjusted accordingly. This process can be supported by various techniques, such as transfer learning or cross-lingual training. The result is language-specific embeddings that better capture the nuances of the target language, thus leading to improved model performance.

Advantages of Learned Embedding Propagation

LEP offers several advantages over complete retraining:

- Lower computational cost and shorter training times - Efficient use of existing resources - Faster adaptation to new languages - Improvement of model performance in the target language

Applications of LEP in Practice

LEP can be used in various areas to improve the performance of language models in other languages. Examples include:

Machine Translation: By adapting the embeddings to the source and target languages, the quality of translation can be improved.

Text Generation: LEP enables the generation of natural language texts in the target language, taking into account language-specific characteristics.

Sentiment Analysis: Adapting the embeddings to the target language improves the accuracy of sentiment analysis in other languages.

Conclusion

Learned Embedding Propagation represents a promising method for efficiently adapting large language models to specific languages. By specifically adapting the embeddings, the models can be adjusted to the nuances of the target language without requiring resource-intensive retraining. This opens up new possibilities for the use of LLMs in various languages and application areas.

Bibliographie: https://arxiv.org/abs/2412.21140 https://arxiv.org/pdf/2412.21140? https://chatpaper.com/chatpaper/ja/paper/95035 https://huggingface.co/RefalMachine/ruadapt_qwen2.5_3B_ext_u48_instruct_v4_gguf https://paperreading.club/page?id=275851 https://huggingface.co/RefalMachine/ruadapt_llama3_8b_instruct_extended_lep_ft https://www.chatpaper.com/chatpaper/de?id=3&date=1735574400&page=1 https://arxiv-sanity-lite.com/?rank=pid&pid=2412.21140 https://russianscdays.org/files/2024/pdf/aiml/6_Tikhomirov.pdf https://arxiv-sanity-lite.com/