The Devil is in the Errors: Leveraging Large Language Models for Fine-grained Machine Translation Evaluation
This addresses the need for more informative evaluation metrics in machine translation, offering an incremental improvement over existing scalar score methods.
The paper tackles the problem of automatic machine translation evaluation lacking detailed error annotation by proposing AutoMQM, a prompting technique using large language models to identify and categorize errors, which improves performance and provides interpretability aligned with human annotations.
Automatic evaluation of machine translation (MT) is a critical tool driving the rapid iterative development of MT systems. While considerable progress has been made on estimating a single scalar quality score, current metrics lack the informativeness of more detailed schemes that annotate individual errors, such as Multidimensional Quality Metrics (MQM). In this paper, we help fill this gap by proposing AutoMQM, a prompting technique which leverages the reasoning and in-context learning capabilities of large language models (LLMs) and asks them to identify and categorize errors in translations. We start by evaluating recent LLMs, such as PaLM and PaLM-2, through simple score prediction prompting, and we study the impact of labeled data through in-context learning and finetuning. We then evaluate AutoMQM with PaLM-2 models, and we find that it improves performance compared to just prompting for scores (with particularly large gains for larger models) while providing interpretability through error spans that align with human annotations.