Papers with quality control protocols
HAT: Hallucination Annotation for Translation (2026.acl-long)
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| Challenge: | Hallucinations in machine translation (MT) outputs are prone to hallucination, authors say . lack of high-quality benchmarks for halluciation detection has hindered MT deployments . |
| Approach: | They propose a dataset that provides annotated hallucination distributions and benchmarks . they use 350,959 span-level annotations across 38 language pairs to analyze hallucis a MT output . |
| Outcome: | The proposed dataset provides high-quality benchmarks for hallucination detection in machine translation . the dataset includes 350,959 span-level annotated samples across 38 language pairs . |