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.
@article{arxiv.2406.09140,
title = {Investigating the translation capabilities of Large Language Models trained on parallel data only},
author = {Javier García Gilabert and Carlos Escolano and Aleix Sant Savall and Francesca De Luca Fornaciari and Audrey Mash and Xixian Liao and Maite Melero},
journal= {arXiv preprint arXiv:2406.09140},
year = {2024}
}
Comments
We release our code at: https://github.com/projecte-aina/Plume