Checklist Engineering Empowers Multilingual LLM Judges
This work addresses the problem of cost and efficiency in multilingual automated text evaluation for NLP researchers, offering an incremental improvement over existing methods.
The paper tackled the limited exploration of LLM-as-a-Judge in multilingual contexts by proposing CE-Judge, a training-free framework using checklist intuition, which generally surpassed baselines and performed on par with GPT-4o across multiple languages and benchmark datasets.
Automated text evaluation has long been a central issue in Natural Language Processing (NLP). Recently, the field has shifted toward using Large Language Models (LLMs) as evaluators-a trend known as the LLM-as-a-Judge paradigm. While promising and easily adaptable across tasks, this approach has seen limited exploration in multilingual contexts. Existing multilingual studies often rely on proprietary models or require extensive training data for fine-tuning, raising concerns about cost, time, and efficiency. In this paper, we propose Checklist Engineering based LLM-as-a-Judge (CE-Judge), a training-free framework that uses checklist intuition for multilingual evaluation with an open-source model. Experiments across multiple languages and three benchmark datasets, under both pointwise and pairwise settings, show that our method generally surpasses the baselines and performs on par with the GPT-4o model.