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

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' output...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2024-02
Main Authors: Hada, Rishav, Gumma, Varun, de Wynter, Adrian, Diddee, Harshita, Ahmed, Mohamed, Choudhury, Monojit, Bali, Kalika, Sitaram, Sunayana
Format: Article
Language:English
Subjects:
Benchmarks
Large language models
Multilingualism
Natural language processing
Scaling up
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2331-8422