Noisy-Channel Minimum Bayes Risk Decoding

📰 ArXiv cs.AI

Learn how Noisy-Channel Minimum Bayes Risk Decoding improves text generation by maximizing expected utility over sampled pseudo-references

advanced Published 7 Jul 2026

Action Steps

Implement Minimum Bayes Risk decoding using a language model
Sample pseudo-references to calculate expected utility scores
Select hypotheses that maximize expected utility over the sampled pseudo-references
Evaluate the generated text using asymmetric metrics like BLEU and COMET
Compare the performance of MBR decoding with traditional MAP decoding

Who Needs to Know This

NLP engineers and researchers can benefit from this technique to generate more robust and higher-quality text, while data scientists can apply this to improve language model performance

Key Insight

💡 MBR decoding can produce more robust text generation by maximizing expected utility over sampled pseudo-references

Key Takeaways

Learn how Noisy-Channel Minimum Bayes Risk Decoding improves text generation by maximizing expected utility over sampled pseudo-references

Full Article

Title: Noisy-Channel Minimum Bayes Risk Decoding

Abstract:
arXiv:2607.05198v1 Announce Type: cross Abstract: Minimum Bayes Risk (MBR) decoding yields more robust and higher-quality text generation than maximum a posteriori (MAP) decoding by selecting hypotheses that maximize expected utility over sampled pseudo-references. However, there exists a discrepancy in the design: hypothesis selection calculates expected utility scores conditioned on given pseudo-references, while commonly used evaluation metrics, e.g., BLEU and COMET, are asymmetric. Therefore

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