The article introduces adaptMLLM, an open-source application designed to streamline the fine-tuning process of Multilingual Language Models for Machine Translation in low-resource languages. It offers improved translation performance compared to baseline models, as demonstrated through various metrics and evaluations.
The advent of Large Language Models (LLMs) and Multilingual Language Models (MLLMs) has revolutionized natural language processing by enabling high-quality translations across multiple languages. The article highlights the significance of these models in enhancing communication and productivity.
Key points include the development of adaptMLLM for fine-tuning MLLMs, its impact on low-resource language pairs like English-Irish and English-Marathi, significant improvements in translation performance observed, and the importance of human evaluation in assessing translation quality.
The study also discusses the environmental impact of AI model development and emphasizes sustainable practices. Additionally, it explores the potential applications of LLMs in various domains such as education, medicine, and computational linguistics.
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