Minimum Bayes Risk

Minimum Bayes Risk (MBR) decoding is a decision-making strategy that selects the best output from a set of candidates by minimizing the expected risk, rather than simply maximizing probability. Current research focuses on improving MBR's efficiency through techniques like matrix completion and confidence-based pruning, as well as exploring its application in diverse areas such as machine translation, text generation, and autonomous vehicle trajectory prediction. The effectiveness of MBR in enhancing the quality and diversity of generated text, particularly when combined with neural metrics like COMET, has significant implications for improving the performance of various natural language processing tasks.