Machine Translation with Large Language Models: Decoder Only vs. Encoder-Decoder
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| Main Authors: | , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866912039298924544 |
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| author | M., Abhinav P. M, SujayKumar Reddy Christopher, Oswald |
| author_facet | M., Abhinav P. M, SujayKumar Reddy Christopher, Oswald |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_13747 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine Translation with Large Language Models: Decoder Only vs. Encoder-Decoder M., Abhinav P. M, SujayKumar Reddy Christopher, Oswald Computation and Language Emerging Technologies Machine Learning 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. |
| title | Machine Translation with Large Language Models: Decoder Only vs. Encoder-Decoder |
| topic | Computation and Language Emerging Technologies Machine Learning |
| url | https://arxiv.org/abs/2409.13747 |