Summary of Machine Translation with Large Language Models: Decoder Only Vs. Encoder-decoder, by Abhinav P.m. et al.
Machine Translation with Large Language Models: Decoder Only vs. Encoder-Decoder
by Abhinav P.M., SujayKumar Reddy M, Oswald Christopher
First submitted to arxiv on: 12 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Emerging Technologies (cs.ET); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper “Machine Translation with Large Language Models: Decoder-only vs. Encoder-Decoder” proposes a multilingual machine translation (MT) model that focuses on Indian regional languages, particularly Telugu, Tamil, and Malayalam. The goal is to develop a model capable of delivering accurate and contextually appropriate translations across various language pairs. To achieve this, the project compares the effectiveness of Decoder-only and Encoder-Decoder architectures in large language models. By optimizing translation quality and efficiency, the study aims to advance cross-linguistic communication. The primary objective is to create a high-quality MT model that can accurately translate texts between Indian regional languages and other languages. This project contributes valuable insights into the effectiveness of different model architectures, paving the way for enhanced cross-linguistic communication tools. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper “Machine Translation with Large Language Models: Decoder-only vs. Encoder-Decoder” is about a special kind of computer program that can translate text from one language to another. The program focuses on languages spoken in India, like Telugu and Tamil. The goal is to make the translation process better by comparing two different ways of doing it. One way uses only the “decoder” part of the program, while the other way uses both the “encoder” and “decoder”. By making the translations more accurate and efficient, the study hopes to help people communicate across languages. |
Keywords
* Artificial intelligence * Decoder * Encoder * Encoder decoder * Translation
