Beyond the Sentence: A Survey on Context-Aware Machine Translation with Large Language Models
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912419394093056 |
|---|---|
| author | Appicharla, Ramakrishna Gain, Baban Pal, Santanu Ekbal, Asif |
| author_facet | Appicharla, Ramakrishna Gain, Baban Pal, Santanu Ekbal, Asif |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_07583 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Beyond the Sentence: A Survey on Context-Aware Machine Translation with Large Language Models Appicharla, Ramakrishna Gain, Baban Pal, Santanu Ekbal, Asif Computation and Language Artificial Intelligence 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. |
| title | Beyond the Sentence: A Survey on Context-Aware Machine Translation with Large Language Models |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2506.07583 |