Investigating the translation capabilities of Large Language Models trained on parallel data only
<|reference_start|>Investigating the translation capabilities of Large Language Models trained on parallel data only: In recent years, Large Language Models (LLMs) have demonstrated exceptional proficiency across a broad spectrum of Natural Language Processing (NLP) tasks, including Machine Translation. However, previous methods predominantly relied on iterative processes such as instruction fine-tuning or continual pre-training, leaving unexplored the challenges of training LLMs solely on parallel data. In this work, we introduce PLUME (Parallel Language Model), a collection of three 2B LLMs featuring varying vocabulary sizes (32k, 128k, and 256k) trained exclusively on Catalan-centric parallel examples. These models perform comparably to previous encoder-decoder architectures on 16 supervised translation directions and 56 zero-shot ones. Utilizing this set of models, we conduct a thorough investigation into the translation capabilities of LLMs, probing their performance, the impact of the different elements of the prompt, and their cross-lingual representation space.<|reference_end|>
@article{gilabert2024investigating,
    title={Investigating the translation capabilities of Large Language Models
  trained on parallel data only},
    author={Javier Garc'ia Gilabert, Carlos Escolano, Aleix Sant Savall,
  Francesca De Luca Fornaciari, Audrey Mash, Xixian Liao, Maite Melero},
    journal={arXiv preprint arXiv:2406.09140},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.09140},
    primaryClass={cs.CL}
}