Recent progress in generative language models has enabled machines to\ngenerate astonishingly realistic texts. While there are many legitimate\napplications of such models, there is also a rising need to distinguish\nmachine-generated texts from human-written ones (e.g., fake news detection).\nHowever, to our best knowledge, there is currently no benchmark environment\nwith datasets and tasks to systematically study the so-called "Turing Test"\nproblem for neural text generation methods. In this work, we present the\nTuringBench benchmark environment, which is comprised of (1) a dataset with\n200K human- or machine-generated samples across 20 labels {Human, GPT-1,\nGPT-2_small, GPT-2_medium, GPT-2_large, GPT-2_xl, GPT-2_PyTorch, GPT-3,\nGROVER_base, GROVER_large, GROVER_mega, CTRL, XLM, XLNET_base, XLNET_large,\nFAIR_wmt19, FAIR_wmt20, TRANSFORMER_XL, PPLM_distil, PPLM_gpt2}, (2) two\nbenchmark tasks -- i.e., Turing Test (TT) and Authorship Attribution (AA), and\n(3) a website with leaderboards. Our preliminary experimental results using\nTuringBench show that FAIR_wmt20 and GPT-3 are the current winners, among all\nlanguage models tested, in generating the most human-like indistinguishable\ntexts with the lowest F1 score by five state-of-the-art TT detection models.\nThe TuringBench is available at: https://turingbench.ist.psu.edu/\n