Minimum Bayes Risk Decoding: How Utility Consensus and Distributional Reranking Outperform Mode-Seeking in Large Language Models
Autoregressive large language models generate text by estimating conditional probability distributions over token sequences. In conventional inference pipelines, generating the final output sequence relies almost entirely on search algorithms designed to locate high-probability trajectories: greedy decoding, beam search, or stochastic sampling with temperature and top-p filtering. However, theoretical and empirical analyses demonstrate that the most probable sequence under a model's distributio
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