Adapting LLMs to Hebrew: Unveiling DictaLM 20 with Enhanced Vocabulary and Instruction Capabilities
<|reference_start|>Adapting LLMs to Hebrew: Unveiling DictaLM 20 with Enhanced Vocabulary and Instruction Capabilities: Training large language models (LLMs) in low-resource languages such as Hebrew poses unique challenges. In this paper, we introduce DictaLM2.0 and DictaLM2.0-Instruct, two LLMs derived from the Mistral model, trained on a substantial corpus of approximately 200 billion tokens in both Hebrew and English. Adapting a pre-trained model to a new language involves specialized techniques that differ significantly from training a model from scratch or further training existing models on well-resourced languages such as English. We outline these novel training methodologies, which facilitate effective learning and adaptation to the linguistic properties of Hebrew. Additionally, we fine-tuned DictaLM2.0-Instruct on a comprehensive instruct dataset to enhance its performance on task-specific instructions. To rigorously evaluate our models, we introduce a new benchmark suite for Hebrew LLM evaluation, covering a diverse set of tasks including Question Answering, Sentiment Analysis, Winograd Schema Challenge, Translation, and Summarization. Our work not only addresses the intricacies of training LLMs in low-resource languages but also proposes a framework that can be leveraged for adapting other LLMs to various non-English languages, contributing to the broader field of multilingual NLP.<|reference_end|>
@article{shmidman2024adapting,
    title={Adapting LLMs to Hebrew: Unveiling DictaLM 2.0 with Enhanced Vocabulary
  and Instruction Capabilities},
    author={Shaltiel Shmidman, Avi Shmidman, Amir DN Cohen, Moshe Koppel},
    journal={arXiv preprint arXiv:2407.07080},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.07080},
    primaryClass={cs.CL}
}