Case-Based Decision-Theoretic Decoding with Quality Memories

• URL: http://arxiv.org/abs/2509.12677v1
• Date: Tue, 16 Sep 2025 05:01:05 GMT
• Title: Case-Based Decision-Theoretic Decoding with Quality Memories
• Authors: Hiroyuki Deguchi, Masaaki Nagata,
• Abstract summary: Minimum Bayes risk (MBR) decoding is a decision rule of text generation.<n>It depends on sample texts drawn from the text generation model.<n>It is difficult to find a hypothesis that correctly captures the knowledge or information of out-of-domain.
• Score: 9.995028045771862
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Minimum Bayes risk (MBR) decoding is a decision rule of text generation, which selects the hypothesis that maximizes the expected utility and robustly generates higher-quality texts than maximum a posteriori (MAP) decoding. However, it depends on sample texts drawn from the text generation model; thus, it is difficult to find a hypothesis that correctly captures the knowledge or information of out-of-domain. To tackle this issue, we propose case-based decision-theoretic (CBDT) decoding, another method to estimate the expected utility using examples of domain data. CBDT decoding not only generates higher-quality texts than MAP decoding, but also the combination of MBR and CBDT decoding outperformed MBR decoding in seven domain De--En and Ja$\leftrightarrow$En translation tasks and image captioning tasks on MSCOCO and nocaps datasets.

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