A Novel Approach to Generative AI Translation

Machine translation, increasingly adopted by companies, involves fine-tuning Large Language Models (LLMs) for tasks like text translation. The author introduces a data curation pipeline for creating a relevant parallel corpus and a new metric to assess the understandability of OpenHathi LLM translations. Studies show that prompting strategies improve the clarity of culturally specific translations. Additionally, adapter-based, parameter-efficient coarse-tuning methods match or exceed the performance of advanced LLMs in reasoning tasks with fewer parameters. As technology advances, translators can maintain quality while reducing costs. However, challenges remain, including inaccuracies and biases in translations for under-resourced languages, but AI translation offers significant potential for grasping context and cultural nuances.