Multilingual Neural Machine Translation (NMT) enables training a single model capable of translating between multiple source and target languages. Traditional approaches use encoder-decoder architectures, while recent advancements explore the use of Large Language Models (LLMs) for Multilingual Machine Translation (MMT). This project investigates:

Performance Comparison: Evaluate the performance of encoder-decoder based MT versus smaller LLMs trained on the same data with similar parameters.

Context Role Quantification: Analyze the impact of context (number of tokens) on translation quality for both architectures.

This project, titled "Machine Translation with Large Language Models: Decoder-only vs. Encoder-Decoder," aims to develop a multilin- gual machine translation (MT) model. Focused on Indian regional languages, especially Tel- ugu, Tamil, and Malayalam, the model seeks to enable accurate and contextually appropriate translations across diverse language pairs. By comparing Decoder-only and Encoder-Decoder architectures, the project aims to optimize translation quality and efficiency, advancing cross- linguistic communication tools.The primary objective is to develop a model capable of delivering high-quality translations that are accurate and contextually appropriate. By leveraging large language models, specifically comparing the effectiveness of Decoder-only and Encoder- Decoder architectures, the project seeks to op- timize translation performance and efficiency across multilingual contexts. Through rigorous experimentation and analysis, this project aims to advance the field of machine translation, contributing valuable insights into the effectiveness of different model architectures and paving the way for enhanced cross-linguistic communication tools.