{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/turingbench-a-benchmark-environment-for","title":"TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation","arxiv_id":"2109.13296","date":"2021-09-27","proceeding":"Findings (EMNLP) 2021 11","authors":["Adaku Uchendu","Zeyu Ma","Thai Le","Rui Zhang","Dongwon Lee"],"abstract":"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/","url_abs":"https://arxiv.org/abs/2109.13296v1","url_pdf":"https://arxiv.org/pdf/2109.13296v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"turingbench-a-benchmark-environment-for","repo_url":"https://github.com/amritabh/chatgpt-as-detector","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"turingbench-a-benchmark-environment-for","repo_url":"https://github.com/amritabh/conda-gen-text-detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"turingbench-a-benchmark-environment-for","repo_url":"https://github.com/MindSpore-paper-code-3/code9/tree/main/transformer_xl","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"authorship-attribution","task_name":"Authorship Attribution"},{"task_slug":"binary-text-classification","task_name":"Binary text classification"},{"task_slug":"fake-news-detection","task_name":"Fake News Detection"},{"task_slug":"text-generation","task_name":"Text Generation"}],"methods":[{"method_slug":"adagrad","method_name":"AdaGrad"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"attention-dropout","method_name":"Attention Dropout"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"ctrl","method_name":"CTRL"},{"method_slug":"cosine-annealing","method_name":"Cosine Annealing"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"gpt-3","method_name":"GPT-3"},{"method_slug":"gradient-clipping","method_name":"Gradient Clipping"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"linear-warmup","method_name":"Linear Warmup"},{"method_slug":"linear-warmup-with-cosine-annealing","method_name":"Linear Warmup With Cosine Annealing"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"test","method_name":"Test"},{"method_slug":"weight-decay","method_name":"Weight Decay"},{"method_slug":"xlm","method_name":"XLM"}],"datasets_introduced":[{"slug":"turingbench","name":"TURINGBENCH","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/binary-text-classification-on-turingbench-1","task":"Binary text classification","dataset":"TURINGBENCH (Turing Test, FAIR_wmt20)","model":"RoBERTa","rank_in_archive_order":2,"of":2,"metrics":{"F1 score":"0.4531"},"uses_additional_data":false},{"leaderboard":"/sota/binary-text-classification-on-turingbench","task":"Binary text classification","dataset":"TURINGBENCH (Turing Test, GPT-3)","model":"RoBERTa","rank_in_archive_order":2,"of":2,"metrics":{"F1 score":"0.5209"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2109.13296","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.13296"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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