Generating Diverse and High-Quality Texts by Minimum Bayes Risk Decoding
Yuu Jinnai Ukyo Honda Tetsuro Morimura Peinan Zhang
One of the most important challenges in text generation systems is to produce
outputs that are not only correct but also diverse. Recently, Minimum
Bayes-Risk (MBR) decoding has gained prominence for generating sentences of the
highest quality among the decoding algorithms. However, existing algorithms
proposed for generating diverse outputs are predominantly based on beam search
or random sampling, thus their output quality is capped by these underlying
methods. In this paper, we investigate an alternative approach -- we develop
diversity-promoting decoding algorithms by enforcing diversity objectives to
MBR decoding. We propose two variants of MBR, Diverse MBR (DMBR) and
$k$-medoids MBR (KMBR), methods to generate a set of sentences with high
quality and diversity. We evaluate DMBR and KMBR on a variety of directed text
generation tasks using encoder-decoder models and a large language model with
prompting. The experimental results show that the proposed method achieves a
better trade-off than the diverse beam search and sampling algorithms.