When Models Lie, We Learn: Multilingual Span-Level Hallucination Detection with PsiloQA (2025.findings-emnlp)
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Elisei Rykov, Kseniia Petrushina, Maksim Savkin, Valerii Olisov, Artem Vazhentsev, Kseniia Titova, Alexander Panchenko, Vasily Konovalov, Julia Belikova
| Challenge: | Existing hallucination detection benchmarks operate at the sequence level and are limited to English . Existing methods lacking fine-grained, multilingual supervision are limited in English based on the sequence . |
| Approach: | They propose a large-scale, multilingual dataset annotated with span-level hallucinations across 14 languages. |
| Outcome: | The proposed dataset annotated with span-level hallucinations across 14 languages is scalable and cost-efficient. |
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