% Use this file for citations not found in the ACL Anthology (contained in "anthology.bib").

@book{Aho:72,
    author  = {Alfred V. Aho and Jeffrey D. Ullman},
    title   = {The Theory of Parsing, Translation and Compiling},
    year    = "1972",
    volume  = "1",
    publisher = {Prentice-Hall},
    address = {Englewood Cliffs, NJ}
}

@book{APA:83,
    author  = {{American Psychological Association}},
    title   = {Publications Manual},
    year    = "1983",
    publisher = {American Psychological Association},
    address = {Washington, DC}
}

@article{Chandra:81,
	author = {Ashok K. Chandra and Dexter C. Kozen and Larry J. Stockmeyer},
	year = "1981",
	title = {Alternation},
	journal = {Journal of the Association for Computing Machinery},
	volume = "28",
	number = "1",
	pages = "114--133",
	doi = "10.1145/322234.322243",
}

@inproceedings{andrew2007scalable,
  title={Scalable training of {L1}-regularized log-linear models},
  author={Andrew, Galen and Gao, Jianfeng},
  booktitle={Proceedings of the 24th International Conference on Machine Learning},
  pages={33--40},
  year={2007},
}

@book{Gusfield:97,
    author  = {Dan Gusfield},
    title   = {Algorithms on Strings, Trees and Sequences},
    year    = "1997",
    publisher = {Cambridge University Press},
    address = {Cambridge, UK}
}

@article{rasooli-tetrault-2015,
    author    = {Mohammad Sadegh Rasooli and Joel R. Tetreault},
    title     = {Yara Parser: {A} Fast and Accurate Dependency Parser},
    journal   = {Computing Research Repository},
    volume    = {arXiv:1503.06733},
    year      = {2015},
    url       = {http://arxiv.org/abs/1503.06733},
    note    = {version 2}
}

@article{Ando2005,
	Acmid = {1194905},
	Author = {Ando, Rie Kubota and Zhang, Tong},
	Issn = {1532-4435},
	Issue_Date = {12/1/2005},
	Journal = {Journal of Machine Learning Research},
	Month = dec,
	Numpages = {37},
	Pages = {1817--1853},
	Publisher = {JMLR.org},
	Title = {A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data},
	Volume = {6},
	Year = {2005}
}

@misc{koike2023outfox,
      title={OUTFOX: LLM-generated Essay Detection through In-context Learning with Adversarially Generated Examples}, 
      author={Ryuto Koike and Masahiro Kaneko and Naoaki Okazaki},
      year={2023},
      eprint={2307.11729},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@inproceedings{wang2024m4,
title = {M4: Multi-generator, Multi-domain, and Multi-lingual Black-Box Machine-Generated Text Detection},
author = {Wang, Yuxia and Mansurov, Jonibek and Ivanov, Petar and Su, Jinyan and Shelmanov, Artem and Tsvigun, Akim and Whitehouse, Chenxi and Afzal, Osama Mohammed and Mahmoud, Tarek and Sasaki, Toru and Arnold, Thomas and Aji, Alham Fikri and Habash, Nizar and Gurevych, Iryna and Nakov, Preslav},
booktitle={Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics
},
address = {Malta},
month = {March},
year={2024}
}

@misc{beltagy2020longformer,
      title={Longformer: The Long-Document Transformer}, 
      author={Iz Beltagy and Matthew E. Peters and Arman Cohan},
      year={2020},
      eprint={2004.05150},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@inproceedings{semeval2024task8,
title = {SemEval-2024 Task 8: Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection},
author = {Wang, Yuxia and Mansurov, Jonibek and Ivanov, Petar and Su, Jinyan and Shelmanov, Artem and Tsvigun, Akim and Whitehouse, Chenxi and Afzal, Osama Mohammed and Mahmoud, Tarek and Puccetti, Giovanni and Arnold, Thomas and Aji, Alham Fikri and Habash, Nizar and Gurevych, Iryna and Nakov, Preslav},
booktitle = {Proceedings of the 18th International Workshop on Semantic Evaluation},
series = {SemEval 2024},
year = {2024},
address = {Mexico, Mexico},
month = {June}
}

@inproceedings{Chen_2016, series={KDD ’16},
   title={XGBoost: A Scalable Tree Boosting System},
   url={http://dx.doi.org/10.1145/2939672.2939785},
   DOI={10.1145/2939672.2939785},
   booktitle={Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
   publisher={ACM},
   author={Chen, Tianqi and Guestrin, Carlos},
   year={2016},
   month=aug, collection={KDD ’16} }

@ARTICLE{708428,
  author={Hearst, M.A. and Dumais, S.T. and Osuna, E. and Platt, J. and Scholkopf, B.},
  journal={IEEE Intelligent Systems and their Applications}, 
  title={Support vector machines}, 
  year={1998},
  volume={13},
  number={4},
  pages={18-28},
  keywords={Support vector machines;Machine learning;Algorithm design and analysis;Pattern recognition;Neural networks;Training data;Polynomials;Kernel;Character recognition;Web pages},
  doi={10.1109/5254.708428}}

@inproceedings{devlin-etal-2019-bert,
    title = "{BERT}: Pre-training of Deep Bidirectional Transformers for Language Understanding",
    author = "Devlin, Jacob  and
      Chang, Ming-Wei  and
      Lee, Kenton  and
      Toutanova, Kristina",
    editor = "Burstein, Jill  and
      Doran, Christy  and
      Solorio, Thamar",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/N19-1423",
    doi = "10.18653/v1/N19-1423",
    pages = "4171--4186",
    abstract = "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7{\%} (4.6{\%} absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).",
}

@misc{mitchell2023detectgpt,
      title={DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature}, 
      author={Eric Mitchell and Yoonho Lee and Alexander Khazatsky and Christopher D. Manning and Chelsea Finn},
      year={2023},
      eprint={2301.11305},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{bao2023fastdetectgpt,
      title={Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvature}, 
      author={Guangsheng Bao and Yanbin Zhao and Zhiyang Teng and Linyi Yang and Yue Zhang},
      year={2023},
      eprint={2310.05130},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{deng2023efficient,
      title={Efficient Detection of LLM-generated Texts with a Bayesian Surrogate Model}, 
      author={Zhijie Deng and Hongcheng Gao and Yibo Miao and Hao Zhang},
      year={2023},
      eprint={2305.16617},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{gehrmann2019gltr,
      title={GLTR: Statistical Detection and Visualization of Generated Text}, 
      author={Sebastian Gehrmann and Hendrik Strobelt and Alexander M. Rush},
      year={2019},
      eprint={1906.04043},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{bhattacharjee2023fighting,
      title={Fighting Fire with Fire: Can ChatGPT Detect AI-generated Text?}, 
      author={Amrita Bhattacharjee and Huan Liu},
      year={2023},
      eprint={2308.01284},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@article{Fagni_2021,
   title={TweepFake: About detecting deepfake tweets},
   volume={16},
   ISSN={1932-6203},
   url={http://dx.doi.org/10.1371/journal.pone.0251415},
   DOI={10.1371/journal.pone.0251415},
   number={5},
   journal={PLOS ONE},
   publisher={Public Library of Science (PLoS)},
   author={Fagni, Tiziano and Falchi, Fabrizio and Gambini, Margherita and Martella, Antonio and Tesconi, Maurizio},
   editor={Liu, Qingzhong},
   year={2021},
   month=may, pages={e0251415} }


@misc{liu2023check,
      title={Check Me If You Can: Detecting ChatGPT-Generated Academic Writing using CheckGPT}, 
      author={Zeyan Liu and Zijun Yao and Fengjun Li and Bo Luo},
      year={2023},
      eprint={2306.05524},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@inproceedings{bhattacharjee-etal-2023-conda,
    title = "{C}on{DA}: Contrastive Domain Adaptation for {AI}-generated Text Detection",
    author = "Bhattacharjee, Amrita  and
      Kumarage, Tharindu  and
      Moraffah, Raha  and
      Liu, Huan",
    editor = "Park, Jong C.  and
      Arase, Yuki  and
      Hu, Baotian  and
      Lu, Wei  and
      Wijaya, Derry  and
      Purwarianti, Ayu  and
      Krisnadhi, Adila Alfa",
    booktitle = "Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = nov,
    year = "2023",
    address = "Nusa Dua, Bali",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.ijcnlp-main.40",
    pages = "598--610",
}

@misc{he2024mgtbench,
      title={MGTBench: Benchmarking Machine-Generated Text Detection}, 
      author={Xinlei He and Xinyue Shen and Zeyuan Chen and Michael Backes and Yang Zhang},
      year={2024},
      eprint={2303.14822},
      archivePrefix={arXiv},
      primaryClass={cs.CR}
}

@misc{liu2023argugpt,
      title={ArguGPT: evaluating, understanding and identifying argumentative essays generated by GPT models}, 
      author={Yikang Liu and Ziyin Zhang and Wanyang Zhang and Shisen Yue and Xiaojing Zhao and Xinyuan Cheng and Yiwen Zhang and Hai Hu},
      year={2023},
      eprint={2304.07666},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@inproceedings{uchendu-etal-2021-turingbench-benchmark,
    title = "{TURINGBENCH}: A Benchmark Environment for {T}uring Test in the Age of Neural Text Generation",
    author = "Uchendu, Adaku  and
      Ma, Zeyu  and
      Le, Thai  and
      Zhang, Rui  and
      Lee, Dongwon",
    editor = "Moens, Marie-Francine  and
      Huang, Xuanjing  and
      Specia, Lucia  and
      Yih, Scott Wen-tau",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.findings-emnlp.172",
    doi = "10.18653/v1/2021.findings-emnlp.172",
    pages = "2001--2016",
    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 GPT-3 and FAIR{\_}wmt20 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: \url{https://turingbench.ist.psu.edu/}",
}

@misc{liu2023unforgeable,
      title={An Unforgeable Publicly Verifiable Watermark for Large Language Models}, 
      author={Aiwei Liu and Leyi Pan and Xuming Hu and Shu'ang Li and Lijie Wen and Irwin King and Philip S. Yu},
      year={2023},
      eprint={2307.16230},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@InProceedings{pmlr-v202-kirchenbauer23a,
  title = 	 {A Watermark for Large Language Models},
  author =       {Kirchenbauer, John and Geiping, Jonas and Wen, Yuxin and Katz, Jonathan and Miers, Ian and Goldstein, Tom},
  booktitle = 	 {Proceedings of the 40th International Conference on Machine Learning},
  pages = 	 {17061--17084},
  year = 	 {2023},
  editor = 	 {Krause, Andreas and Brunskill, Emma and Cho, Kyunghyun and Engelhardt, Barbara and Sabato, Sivan and Scarlett, Jonathan},
  volume = 	 {202},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {23--29 Jul},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v202/kirchenbauer23a/kirchenbauer23a.pdf},
  url = 	 {https://proceedings.mlr.press/v202/kirchenbauer23a.html},
  abstract = 	 {Potential harms of large language models can be mitigated by watermarking model output, i.e., embedding signals into generated text that are invisible to humans but algorithmically detectable from a short span of tokens. We propose a watermarking framework for proprietary language models. The watermark can be embedded with negligible impact on text quality, and can be detected using an efficient open-source algorithm without access to the language model API or parameters. The watermark works by selecting a randomized set of "green" tokens before a word is generated, and then softly promoting use of green tokens during sampling. We propose a statistical test for detecting the watermark with interpretable p-values, and derive an information-theoretic framework for analyzing the sensitivity of the watermark. We test the watermark using a multi-billion parameter model from the Open Pretrained Transformer (OPT) family, and discuss robustness and security.}
}

@inproceedings{zhuang-etal-2021-robustly,
    title = "A Robustly Optimized {BERT} Pre-training Approach with Post-training",
    author = "Zhuang, Liu  and
      Wayne, Lin  and
      Ya, Shi  and
      Jun, Zhao",
    editor = "Li, Sheng  and
      Sun, Maosong  and
      Liu, Yang  and
      Wu, Hua  and
      Liu, Kang  and
      Che, Wanxiang  and
      He, Shizhu  and
      Rao, Gaoqi",
    booktitle = "Proceedings of the 20th Chinese National Conference on Computational Linguistics",
    month = aug,
    year = "2021",
    address = "Huhhot, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://aclanthology.org/2021.ccl-1.108",
    pages = "1218--1227",
    abstract = "In the paper we present a {`}pre-training{'}+{`}post-training{'}+{`}fine-tuning{'} three-stage paradigm which is a supplementary framework for the standard {`}pre-training{'}+{`}fine-tuning{'} languagemodel approach. Furthermore based on three-stage paradigm we present a language modelnamed PPBERT. Compared with original BERT architecture that is based on the standard two-stage paradigm we do not fine-tune pre-trained model directly but rather post-train it on the domain or task related dataset first which helps to better incorporate task-awareness knowl-edge and domain-awareness knowledge within pre-trained model also from the training datasetreduce bias. Extensive experimental results indicate that proposed model improves the perfor-mance of the baselines on 24 NLP tasks which includes eight GLUE benchmarks eight Su-perGLUE benchmarks six extractive question answering benchmarks. More remarkably our proposed model is a more flexible and pluggable model where post-training approach is able to be plugged into other PLMs that are based on BERT. Extensive ablations further validate the effectiveness and its state-of-the-art (SOTA) performance. The open source code pre-trained models and post-trained models are available publicly.",
    language = "English",
}

@misc{touvron2023llama,
      title={Llama 2: Open Foundation and Fine-Tuned Chat Models}, 
      author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov and Soumya Batra and Prajjwal Bhargava and Shruti Bhosale and Dan Bikel and Lukas Blecher and Cristian Canton Ferrer and Moya Chen and Guillem Cucurull and David Esiobu and Jude Fernandes and Jeremy Fu and Wenyin Fu and Brian Fuller and Cynthia Gao and Vedanuj Goswami and Naman Goyal and Anthony Hartshorn and Saghar Hosseini and Rui Hou and Hakan Inan and Marcin Kardas and Viktor Kerkez and Madian Khabsa and Isabel Kloumann and Artem Korenev and Punit Singh Koura and Marie-Anne Lachaux and Thibaut Lavril and Jenya Lee and Diana Liskovich and Yinghai Lu and Yuning Mao and Xavier Martinet and Todor Mihaylov and Pushkar Mishra and Igor Molybog and Yixin Nie and Andrew Poulton and Jeremy Reizenstein and Rashi Rungta and Kalyan Saladi and Alan Schelten and Ruan Silva and Eric Michael Smith and Ranjan Subramanian and Xiaoqing Ellen Tan and Binh Tang and Ross Taylor and Adina Williams and Jian Xiang Kuan and Puxin Xu and Zheng Yan and Iliyan Zarov and Yuchen Zhang and Angela Fan and Melanie Kambadur and Sharan Narang and Aurelien Rodriguez and Robert Stojnic and Sergey Edunov and Thomas Scialom},
      year={2023},
      eprint={2307.09288},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{ouyang2022training,
      title={Training language models to follow instructions with human feedback}, 
      author={Long Ouyang and Jeff Wu and Xu Jiang and Diogo Almeida and Carroll L. Wainwright and Pamela Mishkin and Chong Zhang and Sandhini Agarwal and Katarina Slama and Alex Ray and John Schulman and Jacob Hilton and Fraser Kelton and Luke Miller and Maddie Simens and Amanda Askell and Peter Welinder and Paul Christiano and Jan Leike and Ryan Lowe},
      year={2022},
      eprint={2203.02155},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{muennighoff2023crosslingual,
      title={Crosslingual Generalization through Multitask Finetuning}, 
      author={Niklas Muennighoff and Thomas Wang and Lintang Sutawika and Adam Roberts and Stella Biderman and Teven Le Scao and M Saiful Bari and Sheng Shen and Zheng-Xin Yong and Hailey Schoelkopf and Xiangru Tang and Dragomir Radev and Alham Fikri Aji and Khalid Almubarak and Samuel Albanie and Zaid Alyafeai and Albert Webson and Edward Raff and Colin Raffel},
      year={2023},
      eprint={2211.01786},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}