  We propose a shared task of human-like long text generation, LTG Challenge,
that asks models to output a consistent human-like long text (a Harry Potter
generic audience fanfic in English), given a prompt of about 1000 tokens. We
suggest a novel statistical metric of the text structuredness, GloVe
Autocorrelations Power/ Exponential Law Mean Absolute Percentage Error Ratio
(GAPELMAPER) and a human evaluation protocol. We hope that LTG can open new
avenues for researchers to investigate sampling approaches, prompting
strategies, autoregressive and non-autoregressive text generation architectures
and break the barrier to generate consistent long (40K+ token) texts.
