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One of the most important challenges in text generation systems is to produceoutputs that are not only correct but also diverse. Recently, MinimumBayes-Risk (MBR) decoding has gained prominence for generating sentences of thehighest quality among the decoding algorithms. However, existing algorithmsproposed for generating diverse outputs are predominantly based on beam searchor random sampling, thus their output quality is capped by these underlyingmethods. In this paper, we investigate an alternative approach -- we developdiversity-promoting decoding algorithms by enforcing diversity objectives toMBR decoding. We propose two variants of MBR, Diverse MBR (DMBR) and$k$-medoids MBR (KMBR), methods to generate a set of sentences with highquality and diversity. We evaluate DMBR and KMBR on a variety of directed textgeneration tasks using encoder-decoder models and a large language model withprompting. The experimental results show that the proposed method achieves abetter trade-off than the diverse beam search and sampling algorithms.

Generating Diverse and High-Quality Texts by Minimum Bayes Risk Decoding