Before revision:  Our result is conditional on the widely believed hypothesis that NP } \mathrm{P/poly energy-based ) models may sometimes outperform locally normalized (autoregressive) models , as we demonstrate experimentally for language modeling .
After revision:  Our result is conditional on the widely believed hypothesis that NP } \mathrm{P/poly energy-based ) models may sometimes outperform locally normalized (autoregressive) models (which give up efficient sampling) and latent-variable autoregressive models (which give up efficient scoring of a given string). Both are powerful enough to escape the above limitations .