How I use an LLM as a translation judge
A new method called GEMBA-MQM v2 utilizes large language models to evaluate translation quality, mimicking the detailed error analysis performed by human linguists. This approach categorizes translation errors by type and severity, offering a structured breakdown rather than a single score. While LLM judges can be inconsistent, running multiple passes and aggregating results helps to mitigate this noise and achieve more reliable quality assessments. AI
IMPACT LLM-based translation evaluation offers a scalable alternative to human review, potentially improving translation pipeline efficiency.