Papers with LLMs selection
MQM-APE: Toward High-Quality Error Annotation Predictors with Automatic Post-Editing in LLM Translation Evaluators (2025.coling-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have shown significant potential as judges for Machine Translation (MT) quality assessment. |
| Approach: | They propose a framework that automatically post-edits the original translation based on each error, thereby filtering out non-impactful errors. |
| Outcome: | The proposed framework improves reliability and quality of error spans against GEMBA-MQM, across eight LLMs in both high- and low-resource languages. |