LLMs for Low-Resource Dialect Translation Using Context-Aware Prompting: A Case Study on Sylheti

This article explores the application of Large Language Models (LLMs) for translating a low-resource dialect, Sylheti. The focus is on using context-aware prompting, which suggests the research investigates how providing context to the LLM improves translation accuracy in a resource-constrained setting. The use of a case study indicates a practical, experimental approach to evaluating the effectiveness of the proposed method.

• •Focuses on using LLMs for translating a low-resource dialect (Sylheti).
• •Employs context-aware prompting to improve translation accuracy.
• •Presents a case study, indicating an experimental approach.