@inproceedings{alfano-etal-2026-multilingual,
    title = "Multilingual Self-Taught Faithfulness Evaluators",
    author = "Alfano, Carlo  and
      Al Marjani, Aymen  and
      Jonke, Zeno  and
      Mantrach, Amin  and
      Mansour, Saab  and
      Federico, Marcello",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.266/",
    pages = "5035--5051",
    ISBN = "979-8-89176-386-9",
    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 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."
}