mbrs: A Library for Minimum Bayes Risk Decoding
<|reference_start|>mbrs: A Library for Minimum Bayes Risk Decoding: 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<|reference_end|>
@article{deguchi2024mbrs:,
    title={mbrs: A Library for Minimum Bayes Risk Decoding},
    author={Hiroyuki Deguchi, Yusuke Sakai, Hidetaka Kamigaito, Taro Watanabe},
    journal={arXiv preprint arXiv:2408.04167},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.04167},
    primaryClass={cs.CL}
}