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This paper introduces a reference-free hallucination detection framework for low-resource Sinhala-to-English neural machine translation, presenting a 45,000-sample synthetic corruption dataset and fine-tuning mDeBERTa-v3 to achieve a token-level F1 of 0.841, benchmarked across eight NMT systems.
This paper introduces Approximate Structured Diffusion, a method that combines conditional random fields (CRFs) with discrete diffusion for sequence labelling. It uses a CRF conditioned on noisy label sequences and approximate mean-field inference, achieving a 16.5% error reduction on POS tagging.