Minimum Bayes risk (MBR) decoding is a decision rule of text generation tasks that outperforms conventional maximum a posterior (MAP) decoding using beam search by selecting high-quality outputs based on a utility function rather than those with high-probability. Typically, it finds the most suitable hypothesis from the set of hypotheses under the sampled pseudo-references. mbrs is a library of MBR decoding, which can flexibly combine various metrics, alternative expectation estimations, and algorithmic variants. It is designed with a focus on speed measurement and calling count of code blocks, transparency, reproducibility, and extensibility, which are essential for researchers and developers. We published our mbrs as an MIT-licensed open-source project, and the code is available on GitHub. GitHub: https://github.com/naist-nlp/mbrs
@article{arxiv.2408.04167,
title = {mbrs: A Library for Minimum Bayes Risk Decoding},
author = {Hiroyuki Deguchi and Yusuke Sakai and Hidetaka Kamigaito and Taro Watanabe},
journal= {arXiv preprint arXiv:2408.04167},
year = {2024}
}