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

• URL: http://arxiv.org/abs/2504.01919v2
• Date: Thu, 03 Apr 2025 13:30:35 GMT
• Title: Bridging the Linguistic Divide: A Survey on Leveraging Large Language Models for Machine Translation
• Authors: Baban Gain, Dibyanayan Bandyopadhyay, Asif Ekbal,
• Abstract summary: The advent of Large Language Models (LLMs) has significantly reshaped the landscape of machine translation (MT)<n>We analyze techniques such as few-shot prompting, cross-lingual transfer, and parameter-efficient fine-tuning that enable effective adaptation to under-resourced settings.<n>We discuss persistent challenges such as hallucinations, evaluation inconsistencies, and inherited biases while also evaluating emerging LLM-driven metrics for translation quality.
• Score: 33.08089616645845
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 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.

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