Multilingual Self-Taught Faithfulness Evaluators

• URL: http://arxiv.org/abs/2507.20752v1
• Date: Mon, 28 Jul 2025 12:01:59 GMT
• Title: Multilingual Self-Taught Faithfulness Evaluators
• Authors: Carlo Alfano, Aymen Al Marjani, Zeno Jonke, Amin Mantrach, Saab Mansour, Marcello Federico,
• Abstract summary: Self-Taught Evaluators for Multilingual Faithfulness is a framework that learns exclusively from synthetic multilingual summarization data.<n>Our framework shows improvements over existing baselines, including state-of-the-art English evaluators and machine translation-based approaches.
• Score: 11.200203292660758
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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-the-art English evaluators and machine translation-based approaches.

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