  Recent progress in generative language models has enabled machines to
generate astonishingly realistic texts. While there are many legitimate
applications of such models, there is also a rising need to distinguish
machine-generated texts from human-written ones (e.g., fake news detection).
However, to our best knowledge, there is currently no benchmark environment
with datasets and tasks to systematically study the so-called "Turing Test"
problem for neural text generation methods. In this work, we present the
TuringBench benchmark environment, which is comprised of (1) a dataset with
200K human- or machine-generated samples across 20 labels {Human, GPT-1,
GPT-2_small, GPT-2_medium, GPT-2_large, GPT-2_xl, GPT-2_PyTorch, GPT-3,
GROVER_base, GROVER_large, GROVER_mega, CTRL, XLM, XLNET_base, XLNET_large,
FAIR_wmt19, FAIR_wmt20, TRANSFORMER_XL, PPLM_distil, PPLM_gpt2}, (2) two
benchmark tasks -- i.e., Turing Test (TT) and Authorship Attribution (AA), and
(3) a website with leaderboards. Our preliminary experimental results using
TuringBench show that FAIR_wmt20 and GPT-3 are the current winners, among all
language models tested, in generating the most human-like indistinguishable
texts with the lowest F1 score by five state-of-the-art TT detection models.
The TuringBench is available at: https://turingbench.ist.psu.edu/
