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Neural sequence generation models are known to "hallucinate", by producingoutputs that are unrelated to the source text. These hallucinations arepotentially harmful, yet it remains unclear in what conditions they arise andhow to mitigate their impact. In this work, we first identify internal modelsymptoms of hallucinations by analyzing the relative token contributions to thegeneration in contrastive hallucinated vs. non-hallucinated outputs generatedvia source perturbations. We then show that these symptoms are reliableindicators of natural hallucinations, by using them to design a lightweighthallucination detector which outperforms both model-free baselines and strongclassifiers based on quality estimation or large pre-trained models on manuallyannotated English-Chinese and German-English translation test beds.

Understanding and Detecting Hallucinations in Neural Machine Translation via Model Introspection