对比解码器与编码器-解码器架构，提升印度方言翻译准确率。
Machine Translation with Large Language Models: Decoder Only vs. Encoder-Decoder
- 用大语言模型比较解码器和编码器-解码器结构的翻译表现
- 在泰卢固语、泰米尔语、马拉雅拉姆语等语言对上实现高精度翻译
- 适合关注低资源语言翻译的NLP研究者和应用开发者
本项目旨在开发一种多语言机器翻译（MT）模型，聚焦印度地区语言，尤其是泰卢固语、泰米尔语和马拉雅拉姆语，以实现跨多种语言对的准确且语境恰当的翻译。通过对比解码器仅架构与编码器-解码器架构，项目致力于优化翻译质量与效率，推动跨语言交流工具的发展。核心目标是构建具备高质量、上下文相关翻译能力的模型，利用大语言模型，评估不同架构在多语言环境下的性能与效率。通过严格的实验与分析，本研究为机器翻译领域提供关于模型架构有效性的关键见解，助力更高效的跨语言沟通系统发展。
原文摘要 · Abstract (English)
This project, titled "Machine Translation with Large Language Models: Decoder-only vs. Encoder-Decoder," aims to develop a multilingual machine translation (MT) model. Focused on Indian regional languages, especially Telugu, 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 optimize 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.
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