Year: 2026
Type: conference-paper
Abstract: The growing use of large language models (LLMs) has increased the need for automatic evaluation systems, particularly to address the challenge of information hallucination.Although existing faithfulness evaluation approaches have shown promise, they are predominantly English-focused and often require expensive human-labeled training data for fine-tuning specialized models.As LLMs see increased adoption in multilingual contexts, there is a need for accurate faithfulness evaluators that can operate across languages without extensive labeled data.This paper presents STEMF (Self-Taught Evaluators for Multilingual Faithfulness), a framework that learns exclusively from synthetic multilingual summarization data while leveraging cross-lingual transfer learning.Through experiments comparing language-specific and mixed-language fine-tuning approaches, we demonstrate a consistent relationship between an LLM's general language capabilities and its performance in language-specific evaluation tasks.Our framework shows improvements over existing baselines, including state-of-theart English evaluators and machine translationbased approaches.Our code is available at https://github.com/amazon-science/ multilingual-faithfulness
Institutions: Amazon (United States), University of Oxford
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7140132083