Hacking Generative Perplexity: Why Unconditional Text Evaluation Needs Distributional Metrics
Diffusion and continuous flow-based language models have emerged as the leading non-autoregressive alternatives to language modeling. Progress in both paradigms is overwhelmingly tracked by generative perplexity (gen-PPL): the per-token negative log-likelihood of samples under a frozen autoregressive (AR) scorer such as gpt2-large, typically paired with an empirical-entropy guardrail to rule out low-entropy collapse. We argue that this metric is unsound. By construction, gen-PPL measures only predictability under the scoring AR, not grammaticality or semantic coherence -- and the set of predictable but still low-quality sequences is combinatorially large. To make this concrete, we constru...
| Authors | Antonio Franca, Alexander Tong |
|---|---|
| Primary category | cs.CL |
| Published | 7 June 2026 |
| Last updated at source | 1 August 2026 |
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