Beyond the Sentence: A Survey on Context-Aware Machine Translation with Large Language Models
Beyond the Sentence: A Survey on Context-Aware Machine Translation with Large Language Models
Despite the popularity of the large language models (LLMs), their application to machine translation is relatively underexplored, especially in context-aware settings. This work presents a literature review of context-aware translation with LLMs. The existing works utilise prompting and fine-tuning approaches, with few focusing on automatic post-editing and creating translation agents for context-aware machine translation. We observed that the commercial LLMs (such as ChatGPT and Tower LLM) achieved better results than the open-source LLMs (such as Llama and Bloom LLMs), and prompt-based approaches serve as good baselines to assess the quality of translations. Finally, we present some interesting future directions to explore.
Ramakrishna Appicharla、Baban Gain、Santanu Pal、Asif Ekbal
计算技术、计算机技术
Ramakrishna Appicharla,Baban Gain,Santanu Pal,Asif Ekbal.Beyond the Sentence: A Survey on Context-Aware Machine Translation with Large Language Models[EB/OL].(2025-06-09)[2025-07-23].https://arxiv.org/abs/2506.07583.点此复制
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