[2604.05083] Beyond LLM-as-a-Judge: Deterministic Metrics for Multilingual Generative Text Evaluation

Computer Science > Computation and Language arXiv:2604.05083 (cs) [Submitted on 6 Apr 2026] Title:Beyond LLM-as-a-Judge: Deterministic Metrics for Multilingual Generative Text Evaluation Authors:Firoj Alam, Gagan Bhatia, Sahinur Rahman Laskar, Shammur Absar Chowdhury View a PDF of the paper titled Beyond LLM-as-a-Judge: Deterministic Metrics for Multilingual Generative Text Evaluation, by Firoj Alam and 3 other authors View PDF HTML (experimental) Abstract:While Large Language Models (LLMs) are increasingly adopted as automated judges for evaluating generated text, their outputs are often costly, and highly sensitive to prompt design, language, and aggregation strategies, severely, which limits reproducibility. To address these challenges, we propose \textbf{\textit{OmniScore}}, a family of complementary, deterministic learned metrics developed using small size ($<$1B) parameter models. OmniScore approximates LLM-judge behavior while preserving the low latency and consistency of traditional model-based scoring. We trained the models large-scale synthetic supervision ($\sim$564k instances, in \textbf{107 languages}) and evaluated using 8,617 manually annotated instances. The OmniScore family supports reliable, multi-dimensional scores across a variety of settings, including reference-based, source-grounded, and hybrid evaluations. We evaluate these models across question answering (QA), translation, and summarization in \textbf{6 languages}. Our results demonstrate that lig...