SOL: Measuring Gaps between Text Distributions by Double Sliced Wasserstein Metrics

Abstract:Evaluating text generation requires measuring how well the generated distribution matches the data distribution. For autoregressive models, this is done by the perplexity. Diffusion and flow-based language models can only provide a likelihood bound, whose tightness differs between model families. Sample-based substitutes such as generative perplexity with entropy do not consider the distribution fit. We propose SOL, a distance between text distributions. Each sequence is represented by the empirical measure of its hidden states under a fixed transformer and the distributions of these measures are compared by the double sliced Wasserstein distance. We prove that SOL is a metric if the transformer is injective. Experiments show that SOL detects distributional failures, recovers expected model trends, and provides stable sample-based estimates. We put forward SOL to fill the gap in the current evaluation protocol used for non auto-regressive models. As a first step we use SOL to re-evaluate a variety of models trained on OpenWebText.

Computation and Language (cs.CL); Machine Learning (cs.LG); Machine Learning (stat.ML)

arXiv:2610.06513 [cs.CL]

(or arXiv:2610.06513v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.06513 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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