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Bridging the Linguistic Divide: A Survey on Leveraging Large Language Models for Machine Translation

Published: April 2, 2025 | arXiv ID: 2504.01919v3

By: Baban Gain, Dibyanayan Bandyopadhyay, Asif Ekbal

Potential Business Impact:

Helps computers translate rare languages better.

Business Areas:
Natural Language Processing Artificial Intelligence, Data and Analytics, Software

The advent of Large Language Models (LLMs) has significantly reshaped the landscape of machine translation (MT), particularly for low-resource languages and domains that lack sufficient parallel corpora, linguistic tools, and computational infrastructure. This survey presents a comprehensive overview of recent progress in leveraging LLMs for MT. We analyze techniques such as few-shot prompting, cross-lingual transfer, and parameter-efficient fine-tuning (e.g., LoRA, adapters) that enable effective adaptation to under-resourced settings. The paper also explores synthetic data generation strategies using LLMs, including back-translation and lexical augmentation. Additionally, we compare LLM-based translation with traditional encoder-decoder models across diverse language pairs, highlighting the strengths and limitations of each. We discuss persistent challenges - such as hallucinations, evaluation inconsistencies, and inherited biases, while also evaluating emerging LLM-driven metrics for translation quality. This survey offers practical insights and outlines future directions for building robust, inclusive, and scalable MT systems in the era of large-scale generative models.

Country of Origin
🇮🇳 India

Page Count
15 pages

Category
Computer Science:
Computation and Language