Overfitting Mitigation via Singular Value Decomposition in Minimum Bayes Risk Decoding

Abstract:Minimum Bayes Risk (MBR) decoding enables high-quality text generation by selecting the hypothesis that maximizes a utility metric over sampled pseudo-references. However, it is highly susceptible to metric overfitting: it can irregularly inflate the chosen utility metric at the direct expense of other unoptimized evaluation metrics. To mitigate this, we introduce SVD-MBR, which frames the pairwise utility matrix as a noisy information signal. By computing a low-rank approximation via Singular Value Decomposition (SVD) and retaining only the top-$k$ components, we effectively decouple true consensus from metric noise. Experiments demonstrate that SVD-MBR successfully regularizes decoding, yielding substantial gains across a range of generalized metrics. Furthermore, we reveal that this denoising is metric-dependent: neural metrics encode a robust low-rank consensus ideal for SVD, whereas surface-level metrics struggle to separate signal from metric noise.

Computation and Language (cs.CL)

arXiv:2609.01135 [cs.CL]

(or arXiv:2609.01135v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.01135 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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