@victorweidata690

This project, Llama-Chain-Translate, adapts the GEMBA framework for improved machine translation evaluation and enhancement, specifically targeting low-resource languages (LRLs). The application leverages chain-of-thought prompting techniques using open-source models hosted via Together.ai, enabling reproducibility and transparency. First, it assigns a large language model (LLM) as an evaluator, tasked with assessing the fluency and adequacy of translations. Then, a separate LLM is designated as a translator, using evaluation feedback to produce an improved translation. Future directions include fine-tuning the model, domain adaptation, back-translation, noisy channel reranking, and specific tokenization techniques for LRLs to tackle data scarcity and other LRL challenges.

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