Hallucinations and off-target translation remain unsolved problems in machine translation, especially for low-resource languages and massively multilingual models. In this paper, we introduce methods to mitigate both failure cases with a modified decoding objective, without 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, hypothesising that hallucinations will be similarly probable given either. In language-contrastive decoding, we search for a translation that is probable, but improbable given the wrong language indicator token. In experiments on M2M-100 (418M) and SMaLL-100, we find that these methods effectively suppress hallucinations and off-target translations, improving chrF2 by 1.7 and 1.4 points on average across 57 tested translation directions. In a proof of concept on English--German, we also show that we can suppress off-target translations with the Llama 2 chat models, demonstrating the applicability of the method to machine translation with LLMs. We release our source code at https://github.com/ZurichNLP/ContraDecode.

机器翻译中的幻觉和目标脱靶翻译一直是未解决的问题，特别是对于低资源语言和大规模多语种模型。本文介绍了一种修改解码目标的方法，用于缓解这两种失败情况，而无需重新训练或使用外部模型。在实验证明，这些方法能有效抑制幻觉和目标脱靶翻译，在57个测试的翻译方向上平均提高了1.7和1.4个chrF2分数。在英-德的概念验证中，我们还展示了我们可以使用Llama 2聊天模型来抑制目标脱靶翻译，证明了该方法在LLM机器翻译中的可应用性。

使用源对比和语言对比解码减轻幻觉和目标外机器翻译