Papers with Minimum Bayes Risk
Understanding the Properties of Minimum Bayes Risk Decoding in Neural Machine Translation (2021.acl-long)
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| Challenge: | Neural Machine Translation (NMT) currently exhibits biases such as producing translations that are too short and overgenerating frequent words. |
| Approach: | They propose to use minimum bayes risk decoding instead of beam search to investigate the effects of beam decoding on unbiased samples. |
| Outcome: | The proposed method improves on a number of previously reported biases and failure cases of beam search on unbiased samples. |
Direct Preference Optimization for Neural Machine Translation with Minimum Bayes Risk Decoding (2024.naacl-short)
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| Challenge: | Recent work shows that MBR decoding can significantly improve translation performance of Multilingual Large Language Models. |
| Approach: | They propose a method that uses a monolingual fine-tuning set to fine- tune MLLMs to get the gains of MBR without additional computation in inference. |
| Outcome: | The proposed method outperforms greedy decoding and beam search on multiple NMT tests. |
High Quality Rather than High Model Probability: Minimum Bayes Risk Decoding with Neural Metrics (2022.tacl-1)
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| Challenge: | Neural machine translations are ranked below human translations in professional evaluations . |
| Approach: | They apply minimum bayes risk decoding to optimize different metrics of translation quality . they show that model estimates and translation quality only vaguely correlate . |
| Outcome: | The proposed method improves human translations with different models and metric. |
Unveiling the Power of Source: Source-based Minimum Bayes Risk Decoding for Neural Machine Translation (2025.acl-long)
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| Challenge: | Maximum a posteriori decoding aims to maximize the estimated posterior probability, but high estimated probability does not always lead to high translation quality. |
| Approach: | They propose a method that seeks hypotheses with the highest expected utility by using quasi-sources as “support hypothese . they propose sMBR decoding which utilizes a reference-free quality estimation metric as the utility function. |
| Outcome: | The proposed approach outperforms QE reranking and the standard MBR decoding. |
Regularized Best-of-N Sampling with Minimum Bayes Risk Objective for Language Model Alignment (2025.naacl-long)
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| Challenge: | Best-of-N (BoN) sampling is an effective strategy for aligning Large Language Models (LLMs) to human preferences at the time of decoding. |
| Approach: | They propose a variant of BoN that incorporates the Minimum Bayes Risk objective as a proximity regularizer for BoN sampling. |
| Outcome: | The proposed method outperforms both BoN sampling and MBR decoding on the AlpacaFarm and Anthropic datasets. |
Epsilon Sampling Rocks: Investigating Sampling Strategies for Minimum Bayes Risk Decoding for Machine Translation (2023.findings-emnlp)
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| Challenge: | Recent advances in machine translation (MT) have shown that minimum bayes risk decoding can be a powerful alternative to beam search. |
| Approach: | They propose to use epsilon-sampling to prune away all tokens with a smaller probability mass. |
| Outcome: | The proposed method outperforms beam search decoding and other methods in four languages. |
Theoretical Guarantees for Minimum Bayes Risk Decoding (2025.acl-long)
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| Challenge: | Minimum Bayes Risk (MBR) decoding is a decision rule used to generate sequences from autoregressive probability models (e.g., LLMs). |
| Approach: | They propose to use minimum bayes risk (MBR) decoding to optimize output selection by maximizing expected utility value of an underlying human distribution. |
| Outcome: | The proposed method is effective even though the language space Y is larger than the hypothesis set. |
Digging Errors in NMT: Evaluating and Understanding Model Errors from Partial Hypothesis Space (2022.emnlp-main)
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| Challenge: | Current evaluation of neural machine translation systems is limited by one best hypothesis and search errors brought by heuristic decoding algorithms. |
| Approach: | They propose a new evaluation protocol which defines model errors with model’s ranking capability over hypothesis space and Monte Carlo sampling evaluation to tackle the problem of exponentially large space. |
| Outcome: | The proposed evaluation protocol is consistent with what is currently used in the field and is consistent to what is being proposed. |
Improving Minimum Bayes Risk Decoding with Multi-Prompt (2024.emnlp-main)
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| Challenge: | Existing methods to generate LLMs with a single ‘best’ prompt are unstable and sub-optimal in practice. |
| Approach: | They propose to decode multiple candidate generations from a prompt bank at inference-time and use Minimum Bayes Risk (MBR) to select a final output. |
| Outcome: | The proposed method improves MBR across a set of conditional generation tasks and models. |
Document-Level Text Generation with Minimum Bayes Risk Decoding using Optimal Transport (2025.acl-long)
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| Challenge: | Document-level text generation tasks are more difficult than sentence-level ones as they require an understanding of longer context to generate high-quality texts. |
| Approach: | They propose a variant of minimum bayes risk decoding that uses a sentence-level utility function to compute the utility of a document. |
| Outcome: | The proposed decoding outperforms the standard MBR in document-level tasks. |
Diversity Explains Inference Scaling Laws: Through a Case Study of Minimum Bayes Risk Decoding (2025.acl-long)
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| Challenge: | Existing studies show that MBR decoding improves model generation performance . however, the theoretical underpinnings of these results remain uncertain . |
| Approach: | They propose a theoretical interpretation of MBR decoding from the perspective of bias–diversity decomposition. |
| Outcome: | The proposed method improves the quality estimation of hypotheses by decomposing bias and diversity into two main factors. |
Structure-Conditional Minimum Bayes Risk Decoding (2025.emnlp-main)
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| Challenge: | Minimum Bayes Risk (MBR) decoding has been used in machine translation for many years. |
| Approach: | They propose three adaptations to the minimum bayes risk utility function to make it more sensitive to structural variability in the outcome space. |
| Outcome: | The proposed adaptations significantly improve generation quality by up to 13.7 percentage points in win rate. |
Enhancing Factuality through Consensus and Consistency in Summarization Using Minimum Bayes Risk Decoding (2026.findings-acl)
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Riza Setiawan Soetedjo, Yusuke Sakai, Hidetaka Kamigaito, Jingun Kwon, Manabu Okumura, Taro Watanabe
| Challenge: | Existing methods to improve summarization quality are limited to using source as guidance . reranking can be effective, but there are limitations, such as relying on reference-free metrics and rely on a single metric. |
| Approach: | They propose a model that reranks model-generated summaries by considering consistency to the source document and consensus among the other candidates. |
| Outcome: | The proposed system is competitive with existing methods, with human evaluations further confirming that it is superior. |