# Agreement-Constrained Probabilistic Minimum Bayes Risk Decoding

arXiv: https://arxiv.org/abs/2512.01316 · HTML: https://mlsanity.com/paper/2512.01316/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 22 references that are in the map.
- First posted on arXiv 1 Dec 2025.

## Builds on (most cited first, 16 of 16)

- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (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)
- [Learning Compact Metrics for MT](https://mlsanity.com/paper/2110.06341/) (2021)
- [MetricX-24: The Google Submission to the WMT 2024 Metrics Shared Task](https://mlsanity.com/paper/2410.03983/) (2024)
- [Sampling-Based Approximations to Minimum Bayes Risk Decoding for Neural Machine Translation](https://mlsanity.com/paper/2108.04718/) (2021)
- [Epsilon Sampling Rocks: Investigating Sampling Strategies for Minimum Bayes Risk Decoding for Machine Translation](https://mlsanity.com/paper/2305.09860/) (2023)
- [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)
- [Hyperparameter-Free Approach for Faster Minimum Bayes Risk Decoding](https://mlsanity.com/paper/2401.02749/) (2024)
- [Efficient Minimum Bayes Risk Decoding using Low-Rank Matrix Completion Algorithms](https://mlsanity.com/paper/2406.02832/) (2024)
- [mbrs: A Library for Minimum Bayes Risk Decoding](https://mlsanity.com/paper/2408.04167/) (2024)
- [Centroid-Based Efficient Minimum Bayes Risk Decoding](https://mlsanity.com/paper/2402.11197/) (2024)

## Built on by (most cited first, 2 of 2)

- [Enhancing Factuality through Consensus and Consistency in Summarization Using Minimum Bayes Risk Decoding](https://mlsanity.com/paper/2605.29336/) (2026)
- [RARF: Region-Aware Rectified Flows for 3D Brain MRI Inpainting](https://mlsanity.com/paper/2609.03956/) (2026)

## Abstract (arXiv)

Minimum Bayes risk (MBR) decoding generates high-quality translations by maximizing the expected utility of output candidates, but it evaluates all pairwise scores over the candidate set; hence, it takes quadratic time with respect to the number of candidates. To reduce the number of utility function calls, probabilistic MBR (PMBR) decoding partially evaluates quality scores using sampled pairs of candidates and completes the missing scores with a matrix completion algorithm. Nevertheless, it degrades the translation quality as the number of utility function calls is reduced. Therefore, to improve the trade-off between quality and cost, we propose agreement-constrained PMBR (AC-PMBR) decoding, which leverages a knowledge distilled model to guide the completion of the score matrix. Our AC-PMBR decoding improved approximation errors of matrix completion by up to 3 times and achieved higher translation quality compared with PMBR decoding at a comparable computational cost on the WMT'23 EnleftrightarrowDe translation tasks.