Papers by Kaito Ariu
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. |
Filtered Direct Preference Optimization (2024.emnlp-main)
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| Challenge: | Existing studies on the impact of RLHF on text quality have focused on reward-model-free RL. |
| Approach: | They propose an extension of direct preference optimization to improve model performance by analyzing the quality of the preference dataset. |
| Outcome: | The proposed method improves the performance of models optimized with DPO over those optimized with reward-model-based RLHF. |
Hyperparameter-Free Approach for Faster Minimum Bayes Risk Decoding (2024.findings-acl)
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| Challenge: | Minimum Bayes-Risk (MBR) decoding is a powerful alternative to beam search for text generation tasks. |
| Approach: | They propose a method that uses a hyperparameter-free method to run MBR decoding efficiently. |
| Outcome: | The proposed method reduces the amount of computation but requires hyperparameter tuning to be effective. |
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. |