Beyond LLM-as-a-Judge: Deterministic Metrics for Multilingual Generative Text Evaluation
arXiv ·
Researchers have developed OmniScore, a family of deterministic learned metrics designed to evaluate generative text as an alternative to Large Language Models (LLMs) used as judges. OmniScore leverages small parameter models (<1B) and was trained on approximately 564,000 synthetic instances across 107 languages, then evaluated using 8,617 manually annotated instances. It approximates LLM-judge behavior while offering low latency and consistency for various evaluation settings like reference-based and source-grounded assessments in tasks like QA, translation, and summarization. Why it matters: This development provides a practical, scalable, and reproducible method for multilingual generative text evaluation, addressing key limitations of LLM-as-a-judge approaches and offering significant benefits for AI development in linguistically diverse regions.