  Minimum Bayes Risk (MBR) decoding has been shown to be a powerful alternative
to beam search decoding in a variety of text generation tasks. MBR decoding
selects a hypothesis from a pool of hypotheses that has the least expected risk
under a probability model according to a given utility function. Since it is
impractical to compute the expected risk exactly over all possible hypotheses,
two approximations are commonly used in MBR. First, it integrates over a
sampled set of hypotheses rather than over all possible hypotheses. Second, it
estimates the probability of each hypothesis using a Monte Carlo estimator.
While the first approximation is necessary to make it computationally feasible,
the second is not essential since we typically have access to the model
probability at inference time. We propose Model-Based MBR (MBMBR), a variant of
MBR that uses the model probability itself as the estimate of the probability
distribution instead of the Monte Carlo estimate. We show analytically and
empirically that the model-based estimate is more promising than the Monte
Carlo estimate in text generation tasks. Our experiments show that MBMBR
outperforms MBR in several text generation tasks, both with encoder-decoder
models and with large language models.
