Iterative Translation Refinement with Large Language Models

Understand

We propose iteratively prompting a large language model to self-correct a translation, with inspiration from their strong language understanding and translation capability as well as a human-like translation approach.

• Interestingly, multi-turn querying reduces the output's string-based metric scores, but neural metrics suggest comparable or improved quality.
• Human evaluations indicate better fluency and naturalness compared to initial translations and even human references, all while maintaining quality.
• Ablation studies underscore the importance of anchoring the refinement to the source and a reasonable seed translation for quality considerations.