Are Large Language Model-based Evaluators the Solution to Scaling Up Multilingual Evaluation?
This addresses the challenge of scaling multilingual evaluation for NLP researchers, but it is incremental as it focuses on calibrating existing methods rather than introducing a new paradigm.
The study tackled the problem of inadequate evaluation for large language models in languages beyond the top 20 by exploring GPT-4 as an evaluator, finding that calibration with 20K human judgments is necessary to address biases, especially in low-resource languages.
Large Language Models (LLMs) excel in various Natural Language Processing (NLP) tasks, yet their evaluation, particularly in languages beyond the top $20$, remains inadequate due to existing benchmarks and metrics limitations. Employing LLMs as evaluators to rank or score other models' outputs emerges as a viable solution, addressing the constraints tied to human annotators and established benchmarks. In this study, we explore the potential of LLM-based evaluators, specifically GPT-4 in enhancing multilingual evaluation by calibrating them against $20$K human judgments across three text-generation tasks, five metrics, and eight languages. Our analysis reveals a bias in GPT4-based evaluators towards higher scores, underscoring the necessity of calibration with native speaker judgments, especially in low-resource and non-Latin script languages, to ensure accurate evaluation of LLM performance across diverse languages.