Papers with synthetically generated hallucinations
Multi-Hall-SA: A Cross-lingual Benchmark for Multi-Type Hallucination Detection in Low-Resource South African Languages (2026.findings-eacl)
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| Challenge: | Large Language Models generate false or unsupported information, which can be difficult to detect in low-resource languages. |
| Approach: | They propose a cross-lingual benchmark for hallucination detection spanning English and South African languages. |
| Outcome: | The proposed model detects 23.6% fewer hallucinations in South African languages compared to English . human validation confirms the quality and cross-lingual alignment of the model . |