Mitigating Hallucinations and Off-target Machine Translation with Source-Contrastive and Language-Contrastive Decoding
Rico Sennrich, Jannis Vamvas, Alireza Mohammadshahi
Abstract
Hallucinations and off-target translation remain unsolved problems in MT, especially for lowresource languages and massively multilingual models.In this paper, we introduce two related methods to mitigate these failure cases with a modified decoding objective, without either requiring retraining or external models.In source-contrastive decoding, we search for a translation that is probable given the correct input, but improbable given a random input segment.In language-contrastive decoding, we search for a translation that is probable, but improbable given the wrong language indicator token.Experiments on the massively multilingual models M2M-100 (418M) and SMaLL-100 show that these methods suppress hallucinations and off-target translations, reducing the number of translations with segment-level chrF2 below 10 by 67-83% on average, and the number of translations with oscillatory hallucinations by 75-92% on average, across 57 tested translation directions.In a proof of concept on out-of-English translation, we also show that we can suppress off-target translations with large language models.We release our source code.