% Use this file for citations not found in the ACL Anthology (contained in "anthology.bib").
@misc{tian2023gptzero, 
publisher = {GPTZero}, 
url = {https://gptzero.me}, 
year = {2023}, 
author = {Tian, Edward and Cui, Alexander}, 
title = {GPTZero: Towards detection of AI-generated text using zero-shot and supervised methods"}
}

@inproceedings{ostyakova-etal-2023-chatgpt,
    title = "{C}hat{GPT} vs. Crowdsourcing vs. Experts: Annotating Open-Domain Conversations with Speech Functions",
    author = "Ostyakova, Lidiia  and
      Smilga, Veronika  and
      Petukhova, Kseniia  and
      Molchanova, Maria  and
      Kornev, Daniel",
    editor = "Stoyanchev, Svetlana  and
      Joty, Shafiq  and
      Schlangen, David  and
      Dusek, Ondrej  and
      Kennington, Casey  and
      Alikhani, Malihe",
    booktitle = "Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2023",
    address = "Prague, Czechia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.sigdial-1.23",
    doi = "10.18653/v1/2023.sigdial-1.23",
    pages = "242--254",
    abstract = "This paper deals with the task of annotating open-domain conversations with speech functions. We propose a semi-automated method for annotating dialogs following the topic-oriented, multi-layered taxonomy of speech functions with the use of hierarchical guidelines using Large Language Models. These guidelines comprise simple questions about the topic and speaker change, sentence types, pragmatic aspects of the utterance, and examples that aid untrained annotators in understanding the taxonomy. We compare the results of dialog annotation performed by experts, crowdsourcing workers, and ChatGPT. To improve the performance of ChatGPT, several experiments utilising different prompt engineering techniques were conducted. We demonstrate that in some cases large language models can achieve human-like performance following a multi-step tree-like annotation pipeline on complex discourse annotation, which is usually challenging and costly in terms of time and money when performed by humans.",
}


@inproceedings{ostyakova2023linguistic,
  title={Linguistic Annotation Generation with ChatGPT: a Synthetic Dataset of Speech Functions for Discourse Annotation of Casual Conversations},
  author={Ostyakova, Lidiia and PetukhovaO, Kseniia and Smilga, Veronika and ZharikovaO, Dilyara},
  booktitle={Proceedings of the International Conference “Dialogue},
  volume={2023},
  year={2023}
}

@InProceedings{chizhikova2022multilingual,
author="Chizhikova, Anastasia
and Konovalov, Vasily
and Burtsev, Mikhail",
editor="Kryzhanovsky, Boris
and Dunin-Barkowski, Witali
and Redko, Vladimir
and Tiumentsev, Yury",
title="Multilingual Case-Insensitive Named Entity Recognition",
booktitle="Advances in Neural Computation, Machine Learning, and Cognitive Research VI",
year="2023",
publisher="Springer International Publishing",
address="Cham",
pages="448--454",
isbn="978-3-031-19032-2",
doi="10.1007/978-3-031-19032-2_46"
}


@inproceedings{burtsev2018deeppavlov,
  title={DeepPavlov: An Open Source Library for Conversational AI},
  author={Burtsev, Mikhail and Seliverstov, Alexander and Airapetyan, Rafael and Arkhipov, Mikhail and Baymurzina, Dilyara and Bushkov, Nickolay and Gureenkova, Olga and Khakhulin, Taras and Kuratov, Yuri and Kuznetsov, Denis and Konovalov, Vasily},
  journal={NIPS 2018},
  booktitle={NIPS},
  url={https://openreview.net/pdf?id=BJzyCF6Vn7},
  year={2018}
}

@inproceedings{konovalov2016collecting,
	address = {Los Angeles},
	title = {Collecting {Better} {Training} {Data} using {Biased} {Agent} {Policies} in {Negotiation} {Dialogues}},
	url = {http://workshop.colips.org/wochat/documents/RP-270.pdf},
	abstract = {When naturally occurring data is characterized by a highly skewed class distribution, supervised learning often benefits from reducing this skew. Human-agent dialogue data is commonly highly skewed when using standard agent policies. Hence, we suggest that agent policies need to be reconsidered in the context of training data collection. Specifically, in this work we implemented biased agent policies that are optimized for data collection in the negotiation domain. Empirical evaluations show that our method is successful in collecting a reasonably balanced corpus in the highly skewed Job-Candidate domain. Furthermore, using this balanced corpus to train a negotiation intent classifier yields notable performance improvements relative to naturally distributed data.},
	booktitle = {Proceedings of {WOCHAT}, the {Second} {Workshop} on {Chatbots} and {Conversational} {Agent} {Technologies}},
	publisher = {Zerotype},
	author = {Konovalov, Vasily and Melamud, Oren and Artstein, Ron and Dagan, Ido},
	month = sep,
	year = {2016},
	keywords = {Virtual Humans}
}

@misc{he2021deberta,
      title={DeBERTa: Decoding-enhanced BERT with Disentangled Attention}, 
      author={Pengcheng He and Xiaodong Liu and Jianfeng Gao and Weizhu Chen},
      year={2021},
      eprint={2006.03654},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@inproceedings{reimers2019sentencebert,
  title={Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks},
  author={Nils Reimers and Iryna Gurevych},
  booktitle={Conference on Empirical Methods in Natural Language Processing},
  year={2019},
  url={https://api.semanticscholar.org/CorpusID:201646309}
}
@article{Frhling2021FeaturebasedDO,
  title={Feature-based detection of automated language models: tackling GPT-2, GPT-3 and Grover},
  author={Leon Fr{\"o}hling and Arkaitz Zubiaga},
  journal={PeerJ Computer Science},
  year={2021},
  volume={7},
  url={https://api.semanticscholar.org/CorpusID:233563022}
}


@inproceedings{mitchell2023detectgpt, author = {Mitchell, Eric and Lee, Yoonho and Khazatsky, Alexander and Manning, Christopher D. and Finn, Chelsea}, title = {DetectGPT: zero-shot machine-generated text detection using probability curvature}, year = {2023}, booktitle = {Proceedings of the 40th International Conference on Machine Learning}, articleno = {1038}, numpages = {13}, location = {, Honolulu, Hawaii, USA, }, series = {ICML'23} 
}


@inproceedings{manjavacas-etal-2017-assessing,
    title = "Assessing the Stylistic Properties of Neurally Generated Text in Authorship Attribution",
    author = "Manjavacas, Enrique  and
      De Gussem, Jeroen  and
      Daelemans, Walter  and
      Kestemont, Mike",
    editor = "Brooke, Julian  and
      Solorio, Thamar  and
      Koppel, Moshe",
    booktitle = "Proceedings of the Workshop on Stylistic Variation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W17-4914",
    doi = "10.18653/v1/W17-4914",
    pages = "116--125",
}

@inproceedings{kon3,
  title={Effective Paraphrase Expansion in Addressing Lexical Variability},
  author={Konovalov, Vasily and Adler, Meni and Dagan, Ido},
  booktitle={AINL},
  pages={},
  url={https://fruct.org/publications/abstract-AINL-FRUCT-2016/files/Kon.pdf},
  year={2016}
  }


@inproceedings{konovalov_collecting_2016,
	address = {Los Angeles},
	title = {Collecting {Better} {Training} {Data} using {Biased} {Agent} {Policies} in {Negotiation} {Dialogues}},
	abstract = {When naturally occurring data is characterized by a highly skewed class distribution, supervised learning often benefits from reducing this skew. Human-agent dialogue data is commonly highly skewed when using standard agent policies. Hence, we suggest that agent policies need to be reconsidered in the context of training data collection. Specifically, in this work we implemented biased agent policies that are optimized for data collection in the negotiation domain. Empirical evaluations show that our method is successful in collecting a reasonably balanced corpus in the highly skewed Job-Candidate domain. Furthermore, using this balanced corpus to train a negotiation intent classifier yields notable performance improvements relative to naturally distributed data.},
	booktitle = {Proceedings of {WOCHAT}, the {Second} {Workshop} on {Chatbots} and {Conversational} {Agent} {Technologies}},
	publisher = {Zerotype},
	author = {Konovalov, Vasily and Melamud, Oren and Artstein, Ron and Dagan, Ido},
	month = sep,
	year = {2016},
	keywords = {Virtual Humans}
}

@misc{roberta,
  added-at = {2020-12-11T12:52:54.000+0100},
  author = {Liu, Yinhan and Ott, Myle and Goyal, Naman and Du, Jingfei and Joshi, Mandar and Chen, Danqi and Levy, Omer and Lewis, Mike and Zettlemoyer, Luke and Stoyanov, Veselin},
  biburl = {https://www.bibsonomy.org/bibtex/2a4c60811a43da7596716d79b67d26e0a/marjaw},
  interhash = {040474bcd625e7dcc649bb20c81104d2},
  intrahash = {a4c60811a43da7596716d79b67d26e0a},
  keywords = {},
  timestamp = {2020-12-11T12:52:54.000+0100},
  title = {RoBERTa: A Robustly Optimized BERT Pretraining Approach},
  url = {http://arxiv.org/abs/1907.11692},
  year = 2019
}

@misc{touvron2023llama,
      title={LLaMA: Open and Efficient Foundation Language Models}, 
      author={Hugo Touvron and Thibaut Lavril and Gautier Izacard and Xavier Martinet and Marie-Anne Lachaux and Timothée Lacroix and Baptiste Rozière and Naman Goyal and Eric Hambro and Faisal Azhar and Aurelien Rodriguez and Armand Joulin and Edouard Grave and Guillaume Lample},
      year={2023},
      eprint={2302.13971},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}


@article{wang2023m4,
      title={{M4}: Multi-generator, Multi-domain, and Multi-lingual
                   Black-Box Machine-Generated Text Detection}, 
      author={Yuxia Wang and
              Jonibek Mansurov and
              Petar Ivanov and
              Jinyan Su and
              Artem Shelmanov and
              Akim Tsvigun and
              Chenxi Whitehouse and
              Osama Mohammed Afzal and
              Tarek Mahmoud and
              Alham Fikri Aji and
              Preslav Nakov},
      year={2023},
      eprint={2305.14902},
      archivePrefix={arXiv},
      primaryClass={cs.CL}

}


@inproceedings{Cutler2021AutomaticDO,
  title={Automatic Detection of Hybrid Human-Machine Text Boundaries},
  author={Joseph W. Cutler and Liam Dugan and Shreya Havaldar and Adam Stein},
  year={2021},
  url={https://api.semanticscholar.org/CorpusID:248022852}
}

@inproceedings{gltr,
    title = "{GLTR}: Statistical Detection and Visualization of Generated Text",
    author = "Gehrmann, Sebastian  and
      Strobelt, Hendrik  and
      Rush, Alexander",
    editor = "Costa-juss{\`a}, Marta R.  and
      Alfonseca, Enrique",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P19-3019",
    doi = "10.18653/v1/P19-3019",
    pages = "111--116"}

@misc{openai2023gpt4,
      title={GPT-4 Technical Report}, 
      author={OpenAI},
      year={2023},
      eprint={2303.08774},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}


@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}
}



@inbook{zellers2020defending,
author = {Zellers, Rowan and Holtzman, Ari and Rashkin, Hannah and Bisk, Yonatan and Farhadi, Ali and Roesner, Franziska and Choi, Yejin},
title = {Defending against neural fake news},
year = {2019},
publisher = {Curran Associates Inc.},
address = {Red Hook, NY, USA},

booktitle = {Proceedings of the 33rd International Conference on Neural Information Processing Systems},
articleno = {812},
numpages = {12}
}


@misc{ma2023ai,
      title={AI vs. Human -- Differentiation Analysis of Scientific Content Generation}, 
      author={Yongqiang Ma and Jiawei Liu and Fan Yi and Qikai Cheng and Yong Huang and Wei Lu and Xiaozhong Liu},
      year={2023},
      eprint={2301.10416},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}


@misc{zeng2023automatic,
      title={Towards Automatic Boundary Detection for Human-AI Collaborative Hybrid Essay in Education}, 
      author={Zijie Zeng and Lele Sha and Yuheng Li and Kaixun Yang and Dragan Gašević and Guanliang Chen},
      year={2023},
      eprint={2307.12267},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}



@inproceedings{uchendu2021turingbench,
    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",
}


@inproceedings{macko-etal-2023-multitude,
    title = "{MULTIT}u{DE}: Large-Scale Multilingual Machine-Generated Text Detection Benchmark",
    author = "Macko, Dominik  and
      Moro, Robert  and
      Uchendu, Adaku  and
      Lucas, Jason  and
      Yamashita, Michiharu  and
      Pikuliak, Mat{\'u}{\v{s}}  and
      Srba, Ivan  and
      Le, Thai  and
      Lee, Dongwon  and
      Simko, Jakub  and
      Bielikova, Maria",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.emnlp-main.616",
    doi = "10.18653/v1/2023.emnlp-main.616",
    pages = "9960--9987",
}

@inproceedings{kang-etal-2018-dataset,
    title = "A Dataset of Peer Reviews ({P}eer{R}ead): Collection, Insights and {NLP} Applications",
    author = "Kang, Dongyeop  and
      Ammar, Waleed  and
      Dalvi, Bhavana  and
      van Zuylen, Madeleine  and
      Kohlmeier, Sebastian  and
      Hovy, Eduard  and
      Schwartz, Roy",
    editor = "Walker, Marilyn  and
      Ji, Heng  and
      Stent, Amanda",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/N18-1149",
    doi = "10.18653/v1/N18-1149",
    pages = "1647--1661",
}



@inproceedings{Kuznetsov,
  title={Methods for Intrinsic Plagiarism Detection and Author Diarization},
  author={Mikhail P. Kuznetsov and Anastasia Motrenko and Rita Kuznetsova and Vadim V. Strijov},
  booktitle={Conference and Labs of the Evaluation Forum},
  year={2016}
}



@inproceedings{koike2023outfox,
  title={OUTFOX: LLM-generated Essay Detection through In-context Learning with Adversarially Generated Examples},
  author={Ryuto Koike and Masahiro Kaneko and Naoaki Okazaki},
  booktitle={AAAI Conference on Artificial Intelligence},
  year={2023},
  url={https://api.semanticscholar.org/CorpusID:260091573}
}

@inproceedings{clark-etal-2021-thats,
    title = "All That{'}s {`}Human{'} Is Not Gold: Evaluating Human Evaluation of Generated Text",
    author = "Clark, Elizabeth  and
      August, Tal  and
      Serrano, Sofia  and
      Haduong, Nikita  and
      Gururangan, Suchin  and
      Smith, Noah A.",
    editor = "Zong, Chengqing  and
      Xia, Fei  and
      Li, Wenjie  and
      Navigli, Roberto",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.acl-long.565",
    doi = "10.18653/v1/2021.acl-long.565",
}


@misc{dugan2022real,
      title={Real or Fake Text?: Investigating Human Ability to Detect Boundaries Between Human-Written and Machine-Generated Text}, 
      author={Liam Dugan and Daphne Ippolito and Arun Kirubarajan and Sherry Shi and Chris Callison-Burch},
      year={2022},
      eprint={2212.12672},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}




@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).",
}


@inproceedings{gpt3, author = {Brown, Tom B. and Mann, Benjamin and Ryder, Nick and Subbiah, Melanie and Kaplan, Jared and Dhariwal, Prafulla and Neelakantan, Arvind and Shyam, Pranav and Sastry, Girish and Askell, Amanda and Agarwal, Sandhini and Herbert-Voss, Ariel and Krueger, Gretchen and Henighan, Tom and Child, Rewon and Ramesh, Aditya and Ziegler, Daniel M. and Wu, Jeffrey and Winter, Clemens and Hesse, Christopher and Chen, Mark and Sigler, Eric and Litwin, Mateusz and Gray, Scott and Chess, Benjamin and Clark, Jack and Berner, Christopher and McCandlish, Sam and Radford, Alec and Sutskever, Ilya and Amodei, Dario}, title = {Language models are few-shot learners}, year = {2020}, isbn = {9781713829546}, publisher = {Curran Associates Inc.}, address = {Red Hook, NY, USA}, booktitle = {Proceedings of the 34th International Conference on Neural Information Processing Systems}, articleno = {159}, numpages = {25}, location = {Vancouver, BC, Canada}, series = {NIPS'20} }


@inproceedings{ippolito-etal-2020-automatic,
    title = "Automatic Detection of Generated Text is Easiest when Humans are Fooled",
    author = "Ippolito, Daphne  and
      Duckworth, Daniel  and
      Callison-Burch, Chris  and
      Eck, Douglas",
    editor = "Jurafsky, Dan  and
      Chai, Joyce  and
      Schluter, Natalie  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.acl-main.164",
    doi = "10.18653/v1/2020.acl-main.164",
    pages = "1808--1822",
}

@inproceedings{uchendu-etal-2020-authorship,
    title = "Authorship Attribution for Neural Text Generation",
    author = "Uchendu, Adaku  and
      Le, Thai  and
      Shu, Kai  and
      Lee, Dongwon",
    editor = "Webber, Bonnie  and
      Cohn, Trevor  and
      He, Yulan  and
      Liu, Yang",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.emnlp-main.673",
    doi = "10.18653/v1/2020.emnlp-main.673",
    pages = "8384--8395",
}

@article{fagni2021,
author = {Fagni, Tiziano and Falchi, Fabrizio and Gambini, Margherita and Martella, Antonio and Tesconi, Maurizio},
year = {2021},
month = {05},
pages = {e0251415},
title = {TweepFake: About detecting deepfake tweets},
volume = {16},
journal = {PLOS ONE},
doi = {10.1371/journal.pone.0251415}
}



@inproceedings{jawahar-etal-2020-automatic,
    title = "Automatic Detection of Machine Generated Text: A Critical Survey",
    author = "Jawahar, Ganesh  and
      Abdul-Mageed, Muhammad  and
      Lakshmanan, V.S., Laks",
    editor = "Scott, Donia  and
      Bel, Nuria  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://aclanthology.org/2020.coling-main.208",
    doi = "10.18653/v1/2020.coling-main.208",
    pages = "2296--2309",
}

@misc{solaiman2019release,
      title={Release Strategies and the Social Impacts of Language Models}, 
      author={Irene Solaiman and Miles Brundage and Jack Clark and Amanda Askell and Ariel Herbert-Voss and Jeff Wu and Alec Radford and Gretchen Krueger and Jong Wook Kim and Sarah Kreps and Miles McCain and Alex Newhouse and Jason Blazakis and Kris McGuffie and Jasmine Wang},
      year={2019},
      eprint={1908.09203},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}


@inproceedings{rodriguez-etal-2022-cross,
    title = "Cross-Domain Detection of {GPT}-2-Generated Technical Text",
    author = "Rodriguez, Juan Diego  and
      Hay, Todd  and
      Gros, David  and
      Shamsi, Zain  and
      Srinivasan, Ravi",
    editor = "Carpuat, Marine  and
      de Marneffe, Marie-Catherine  and
      Meza Ruiz, Ivan Vladimir",
    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.naacl-main.88",
    doi = "10.18653/v1/2022.naacl-main.88",
    pages = "1213--1233"}

@inproceedings{DeepPavlovSemeval2024task8, 
title={{DeepPavlov} at {SemEval}-2024 Task 8: Leveraging Transfer Learning for Detecting Boundaries of Machine-Generated Texts},
author={Voznyuk, Anastasia and Konovalov, Vasily},
booktitle = {Proceedings of the 18th International Workshop on Semantic Evaluation},
series = {SemEval 2024},
year = {2024},
address = {Mexico City, Mexico},
month = {June},
pages = {},    
doi= {},
misc = {https://github.com/natriistorm/SemEval2024-boundary-detection}            
}

@article{stiff-detecting,
author = {Stiff, Harald and Johansson, Fredrik},
year = {2022},
month = {05},
pages = {},
title = {Detecting computer-generated disinformation},
volume = {13},
journal = {International Journal of Data Science and Analytics},
doi = {10.1007/s41060-021-00299-5}
}

% pick a unique identifier and don’t change it!
@inproceedings{DeepPavlovSemeval2024task8,
title={{DeepPavlov} at {SemEval}-2024 Task 8: Leveraging Transfer Learning for Detecting Boundaries of Machine-Generated Texts},
author={Voznyuk, Anastasia and Konovalov, Vasily},
booktitle = {Proceedings of the 18th International Workshop on Semantic Evaluation},
series = {SemEval 2024},
year = {2024},
address = {Mexico City, Mexico},
month = {June},
pages = {},    
doi= {},
misc = {https://github.com/natriistorm/SemEval2024-boundary-detection}            
}

@article{touvron2023llama2,
  title={Llama 2: Open Foundation and Fine-Tuned Chat Models},
  author={Hugo Touvron and Louis Martin and Kevin R. Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov and Soumya Batra and Prajjwal Bhargava and Shruti Bhosale and Daniel M. Bikel and Lukas Blecher and Cristian Cant{\'o}n 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 S. Hartshorn and Saghar Hosseini and Rui Hou and Hakan Inan and Marcin Kardas and Viktor Kerkez and Madian Khabsa and Isabel M. Kloumann and A. V. 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 R. Subramanian and Xia Tan and Binh Tang and Ross Taylor and Adina Williams and Jian Xiang Kuan and Puxin Xu and Zhengxu 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},
  journal={ArXiv},
  year={2023},
  volume={abs/2307.09288},
  url={https://api.semanticscholar.org/CorpusID:259950998}
}
@misc{he2021debertav3,
      title={DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing}, 
      author={Pengcheng He and Jianfeng Gao and Weizhu Chen},
      year={2021},
      eprint={2111.09543},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{longformer,
      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}, pages = {},
}

@misc{kushnareva2023artificial,
      title={Artificial Text Boundary Detection with Topological Data Analysis and Sliding Window Techniques}, 
      author={Laida Kushnareva and Tatiana Gaintseva and German Magai and Serguei Barannikov and Dmitry Abulkhanov and Kristian Kuznetsov and Irina Piontkovskaya and Sergey Nikolenko},
      year={2023},
      eprint={2311.08349},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@inproceedings{xlnet,
  title={XLNet: Generalized Autoregressive Pretraining for Language Understanding},
  author={Zhilin Yang and Zihang Dai and Yiming Yang and Jaime G. Carbonell and Ruslan Salakhutdinov and Quoc V. Le},
  booktitle={Neural Information Processing Systems},
  year={2019},
  url={https://api.semanticscholar.org/CorpusID:195069387}
}