Papers with M2M models
Contrastive Decoding Reduces Hallucinations in Large Multilingual Machine Translation Models (2024.eacl-long)
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| Challenge: | Hallucinations occur when the target side sentence is detached from the source side sentence, or in other words, when there is a low contribution of the source sentence to the generation of the target sentence. |
| Approach: | They propose to use Contrastive Decoding to maximise the log-likelihood difference between a model and the same model with reduced contribution from the encoder outputs. |
| Outcome: | The proposed algorithm maximises the log-likelihood difference between a model and the same model with reduced contribution from the encoder outputs. |