# mbrs: A Library for Minimum Bayes Risk Decoding

arXiv: https://arxiv.org/abs/2408.04167 · HTML: https://mlsanity.com/paper/2408.04167/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 32 references that are in the map.
- First posted on arXiv 8 Aug 2024.

## Builds on (most cited first, 18 of 18)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [COMET: A Neural Framework for MT Evaluation](https://mlsanity.com/paper/2009.09025/) (2020)
- [Beyond English-Centric Multilingual Machine Translation](https://mlsanity.com/paper/2010.11125/) (2020)
- [Six Challenges for Neural Machine Translation](https://mlsanity.com/paper/1706.03872/) (2017)
- [xCOMET: Transparent Machine Translation Evaluation through Fine-grained Error Detection](https://mlsanity.com/paper/2310.10482/) (2023)
- [Is MAP Decoding All You Need? The Inadequacy of the Mode in Neural Machine Translation](https://mlsanity.com/paper/2005.10283/) (2020)
- [Analyzing Uncertainty in Neural Machine Translation](https://mlsanity.com/paper/1803.00047/) (2018)
- [High Quality Rather than High Model Probability: Minimum Bayes Risk Decoding with Neural Metrics](https://mlsanity.com/paper/2111.09388/) (2021)
- [Sampling-Based Approximations to Minimum Bayes Risk Decoding for Neural Machine Translation](https://mlsanity.com/paper/2108.04718/) (2021)
- [Understanding the Properties of Minimum Bayes Risk Decoding in Neural Machine Translation](https://mlsanity.com/paper/2105.08504/) (2021)
- [Faster Minimum Bayes Risk Decoding with Confidence-based Pruning](https://mlsanity.com/paper/2311.14919/) (2023)
- [Linear-time Minimum Bayes Risk Decoding with Reference Aggregation](https://mlsanity.com/paper/2402.04251/) (2024)
- [Efficient Minimum Bayes Risk Decoding using Low-Rank Matrix Completion Algorithms](https://mlsanity.com/paper/2406.02832/) (2024)
- [Centroid-Based Efficient Minimum Bayes Risk Decoding](https://mlsanity.com/paper/2402.11197/) (2024)
- [Model-Based Minimum Bayes Risk Decoding for Text Generation](https://mlsanity.com/paper/2311.05263/) (2023)
- [Generating Diverse and High-Quality Texts by Minimum Bayes Risk Decoding](https://mlsanity.com/paper/2401.05054/) (2024)

## Built on by (most cited first, 13 of 13)

- [Revisiting Compositional Generalization Capability of Large Language Models Considering Instruction Following Ability](https://mlsanity.com/paper/2506.15629/) (2025)
- [Diversity Explains Inference Scaling Laws: Through a Case Study of Minimum Bayes Risk Decoding](https://mlsanity.com/paper/2410.15021/) (2024)
- [gec-metrics: A Unified Library for Grammatical Error Correction Evaluation](https://mlsanity.com/paper/2505.19388/) (2025)
- [From SALAMANDRA to SALAMANDRATA: BSC Submission for WMT25 General Machine Translation Shared Task](https://mlsanity.com/paper/2508.12774/) (2025)
- [Agreement-Constrained Probabilistic Minimum Bayes Risk Decoding](https://mlsanity.com/paper/2512.01316/) (2025)
- [Case-Based Decision-Theoretic Decoding with Quality Memories](https://mlsanity.com/paper/2509.12677/) (2025)
- [HalluCiteChecker: A Lightweight Toolkit for Hallucinated Citation Detection and Verification in the Era of AI Scientists](https://mlsanity.com/paper/2604.26835/) (2026)
- [Edit-level Majority Voting Mitigates Over-Correction in LLM-based Grammatical Error Correction](https://mlsanity.com/paper/2605.13624/) (2026)
- [Maastricht University at AMIYA: Adapting LLMs for Dialectal Arabic using Fine-tuning and MBR Decoding](https://mlsanity.com/paper/2602.09703/) (2026)
- [Enhancing Factuality through Consensus and Consistency in Summarization Using Minimum Bayes Risk Decoding](https://mlsanity.com/paper/2605.29336/) (2026)
- [MLLP-VRAIN UPV system for the IWSLT 2026 Simultaneous Speech Translation task](https://mlsanity.com/paper/2606.17255/) (2026)
- [Non-Autoregressive Minimum Bayes' Risk Decoding for Fast Speech Recognition](https://mlsanity.com/paper/2606.17537/) (2026)
- [Overfitting Mitigation via Singular Value Decomposition in Minimum Bayes Risk Decoding](https://mlsanity.com/paper/2609.01135/) (2026)

## Abstract (arXiv)

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