QueEn: A Large Language Model for Quechua-English Translation
This work addresses the problem of preserving endangered languages like Quechua through improved translation technology, representing an incremental advance in adapting existing methods to low-resource settings.
The authors tackled the challenge of low-resource language translation by proposing QueEn, a method combining Retrieval-Augmented Generation with parameter-efficient fine-tuning for Quechua-English translation, achieving a BLEU score of 17.6 compared to 1.5 for standard GPT models.
Recent studies show that large language models (LLMs) are powerful tools for working with natural language, bringing advances in many areas of computational linguistics. However, these models face challenges when applied to low-resource languages due to limited training data and difficulty in understanding cultural nuances. In this paper, we propose QueEn, a novel approach for Quechua-English translation that combines Retrieval-Augmented Generation (RAG) with parameter-efficient fine-tuning techniques. Our method leverages external linguistic resources through RAG and uses Low-Rank Adaptation (LoRA) for efficient model adaptation. Experimental results show that our approach substantially exceeds baseline models, with a BLEU score of 17.6 compared to 1.5 for standard GPT models. The integration of RAG with fine-tuning allows our system to address the challenges of low-resource language translation while maintaining computational efficiency. This work contributes to the broader goal of preserving endangered languages through advanced language technologies.