Guiding Large Language Models to Post-Edit Machine Translation with Error Annotations

arXiv:2404.07851v1 Announce Type: new
Abstract: Machine Translation (MT) remains one of the last NLP tasks where large language models (LLMs) have not yet replaced dedicated supervised systems. This work exploits the complementary strengths of LLMs and supervised MT by guiding LLMs to automatically post-edit MT with external feedback on its quality, derived from Multidimensional Quality Metric (MQM) annotations. Working with LLaMA-2 models, we consider prompting strategies varying the nature of feedback provided and then fine-tune the LLM to improve its …

abstract annotations arxiv cs.ai cs.cl edit error exploits feedback language language models large language large language models llms machine machine translation nlp systems tasks translation type work