Are Large Language Model-based Evaluators the Solution to Scaling Up Multilingual Evaluation?

• URL: http://arxiv.org/abs/2309.07462v2
• Date: Tue, 13 Feb 2024 09:10:29 GMT
• Title: Are Large Language Model-based Evaluators the Solution to Scaling Up Multilingual Evaluation?
• Authors: Rishav Hada, Varun Gumma, Adrian de Wynter, Harshita Diddee, Mohamed Ahmed, Monojit Choudhury, Kalika Bali, Sunayana Sitaram
• Abstract summary: Large Language Models (LLMs) excel in various Natural Language Processing (NLP) tasks. Their evaluation, particularly in languages beyond the top $20$, remains inadequate due to existing benchmarks and metrics limitations.
• Score: 20.476500441734427
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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