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

@article{wang2023m4,
    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},
    journal = {ArXiv preprint},
    title = {{M4}: Multi-generator, Multi-domain, and Multi-lingual
Black-Box Machine-Generated Text Detection},
    url = {https://arxiv.org/abs/2305.14902},
    volume = {abs/2305.14902},
    year = {2023}
}

@inproceedings{kang2018dataset,
    address = {New Orleans, Louisiana},
    author = {Kang, Dongyeop  and
Ammar, Waleed  and
Dalvi, Bhavana  and
van Zuylen, Madeleine  and
Kohlmeier, Sebastian  and
Hovy, Eduard  and
Schwartz, Roy},
    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)},
    doi = {10.18653/v1/N18-1149},
    pages = {1647--1661},
    publisher = {Association for Computational Linguistics},
    title = {A Dataset of Peer Reviews ({P}eer{R}ead): Collection, Insights and {NLP} Applications},
    url = {https://aclanthology.org/N18-1149},
    year = {2018}
}

@article{shamardina2022findings,
    author = {Shamardina, Tatiana and Mikhailov, Vladislav and Chernianskii, Daniil and Fenogenova, Alena and Saidov, Marat and Valeeva, Anastasiya and Shavrina, Tatiana and Smurov, Ivan and Tutubalina, Elena and Artemova, Ekaterina},
    journal = {ArXiv preprint},
    title = {Findings of the the ruatd shared task 2022 on artificial text detection in russian},
    url = {https://arxiv.org/abs/2206.01583},
    volume = {abs/2206.01583},
    year = {2022}
}

@misc{openai2023gpt4,
    archiveprefix = {arXiv},
    author = {OpenAI},
    eprint = {2303.08774},
    primaryclass = {cs.CL},
    title = {G{PT}-4 Technical Report},
    year = {2023}
}

@article{touvron2023llama,
    author = {Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and others},
    journal = {ArXiv preprint},
    title = {{LLaMA}: Open and efficient foundation language models},
    url = {https://arxiv.org/abs/2302.13971},
    volume = {abs/2302.13971},
    year = {2023}
}

@misc{touvron2023llama2,
    archiveprefix = {arXiv},
    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},
    eprint = {2307.09288},
    primaryclass = {cs.CL},
    title = {Llama 2: Open Foundation and Fine-Tuned Chat Models},
    year = {2023}
}

@article{chung2022scaling,
    author = {Chung, Hyung Won and Hou, Le and Longpre, Shayne and Zoph, Barret and Tay, Yi and Fedus, William and Li, Eric and Wang, Xuezhi and Dehghani, Mostafa and Brahma, Siddhartha and others},
    journal = {ArXiv preprint},
    title = {Scaling instruction-finetuned language models},
    url = {https://arxiv.org/abs/2210.11416},
    volume = {abs/2210.11416},
    year = {2022}
}

@article{muennighoff2022crosslingual,
    author = {Muennighoff, Niklas and Wang, Thomas and Sutawika, Lintang and Roberts, Adam and Biderman, Stella and Scao, Teven Le and Bari, M Saiful and Shen, Sheng and Yong, Zheng-Xin and Schoelkopf, Hailey and others},
    journal = {ArXiv preprint},
    title = {Crosslingual generalization through multitask finetuning},
    url = {https://arxiv.org/abs/2211.01786},
    volume = {abs/2211.01786},
    year = {2022}
}

@misc{guo2023close,
    archiveprefix = {arXiv},
    author = {Biyang Guo and Xin Zhang and Ziyuan Wang and Minqi Jiang and Jinran Nie and Yuxuan Ding and Jianwei Yue and Yupeng Wu},
    eprint = {2301.07597},
    primaryclass = {cs.CL},
    title = {How Close is {C}hat{GPT} to Human Experts? Comparison Corpus, Evaluation, and Detection},
    year = {2023}
}

@misc{shijaku2023chatgpt,
    author = {Shijaku, Rexhep and Canhasi, Ercan},
    doi = {10.13140/RG.2.2.21317.52960},
    pages = {},
    title = {Chat{GPT} Generated Text Detection},
    year = {2023}
}

@misc{tang2023science,
    archiveprefix = {arXiv},
    author = {Ruixiang Tang and Yu-Neng Chuang and Xia Hu},
    eprint = {2303.07205},
    primaryclass = {cs.CL},
    title = {The Science of Detecting {LLM}-Generated Texts},
    year = {2023}
}

@misc{liu2022coco,
    archiveprefix = {arXiv},
    author = {Xiaoming Liu and Zhaohan Zhang and Yichen Wang and Yu Lan and Chao Shen},
    eprint = {2212.10341},
    primaryclass = {cs.CL},
    title = {Co{C}o: Coherence-Enhanced Machine-Generated Text Detection Under Data Limitation With Contrastive Learning},
    year = {2022}
}

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

@inproceedings{bergsma2012stylometric,
    address = {Montr{\'e}al, Canada},
    author = {Bergsma, Shane  and
Post, Matt  and
Yarowsky, David},
    booktitle = {Proceedings of the 2012 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies},
    pages = {327--337},
    publisher = {Association for Computational Linguistics},
    title = {Stylometric Analysis of Scientific Articles},
    url = {https://aclanthology.org/N12-1033},
    year = {2012}
}

@article{mann1988rhetorical,
    author = {Mann, William C. and Thompson, Sandra A.},
    journal = {Text-interdisciplinary Journal for the Study of Discourse},
    number = {3},
    pages = {243--281},
    publisher = {De Gruyter Mouton},
    title = {Rhetorical structure theory: Toward a functional theory of text organization},
    volume = {8},
    year = {1988}
}

@inproceedings{fiacco2022toward,
    address = {Seattle, Washington},
    author = {Fiacco, James  and
Jiang, Shiyan  and
Adamson, David  and
Ros{\'e}, Carolyn},
    booktitle = {Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022)},
    doi = {10.18653/v1/2022.bea-1.25},
    pages = {204--215},
    publisher = {Association for Computational Linguistics},
    title = {Toward Automatic Discourse Parsing of Student Writing Motivated by Neural Interpretation},
    url = {https://aclanthology.org/2022.bea-1.25},
    year = {2022}
}

@inproceedings{lin2019unified,
    address = {Florence, Italy},
    author = {Lin, Xiang  and
Joty, Shafiq  and
Jwalapuram, Prathyusha  and
Bari, M Saiful},
    booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
    doi = {10.18653/v1/P19-1410},
    pages = {4190--4200},
    publisher = {Association for Computational Linguistics},
    title = {A Unified Linear-Time Framework for Sentence-Level Discourse Parsing},
    url = {https://aclanthology.org/P19-1410},
    year = {2019}
}

@article{mccarthy2010mtld,
    author = {McCarthy, Philip and Jarvis, Scott},
    doi = {10.3758/BRM.42.2.381},
    journal = {Behavior research methods},
    pages = {381-92},
    title = {{MTLD}, {vocd-D}, and {HD-D}: A validation study of sophisticated approaches to lexical diversity assessment},
    volume = {42},
    year = {2010}
}

@article{Frhling2021FeaturebasedDO,
    author = {Leon Fr{\"o}hling and Arkaitz Zubiaga},
    journal = {PeerJ Computer Science},
    title = {Feature-based detection of automated language models: tackling {GPT}-2, {GPT}-3 and {G}rover},
    url = {https://api.semanticscholar.org/CorpusID:233563022},
    volume = {7},
    year = {2021}
}

@misc{kumarage2023neural,
    archiveprefix = {arXiv},
    author = {Tharindu Kumarage and Huan Liu},
    eprint = {2308.07305},
    primaryclass = {cs.CL},
    title = {Neural Authorship Attribution: Stylometric Analysis on Large Language Models},
    year = {2023}
}

@article{su2023detectllm,
    author = {Su, Jinyan and Zhuo, Terry Yue and Wang, Di and Nakov, Preslav},
    journal = {ArXiv preprint},
    title = {{DetectLLM}: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text},
    url = {https://arxiv.org/abs/2306.05540},
    volume = {abs/2306.05540},
    year = {2023}
}

@article{yang2023survey,
    author = {Yang, Xianjun and Pan, Liangming and Zhao, Xuandong and Chen, Haifeng and Petzold, Linda and Wang, William Yang and Cheng, Wei},
    journal = {ArXiv preprint},
    title = {A Survey on Detection of {LLMs}-Generated Content},
    url = {https://arxiv.org/abs/2310.15654},
    volume = {abs/2310.15654},
    year = {2023}
}

@misc{liu2019roberta,
    archiveprefix = {arXiv},
    author = {Yinhan Liu and Myle Ott and Naman Goyal and Jingfei Du and Mandar Joshi and Danqi Chen and Omer Levy and Mike Lewis and Luke Zettlemoyer and Veselin Stoyanov},
    eprint = {1907.11692},
    primaryclass = {cs.CL},
    title = {{RoBERTa}: A Robustly Optimized {BERT} Pretraining Approach},
    year = {2019}
}

@inproceedings{paszke2019pytorch,
    author = {Adam Paszke and
Sam Gross and
Francisco Massa and
Adam Lerer and
James Bradbury and
Gregory Chanan and
Trevor Killeen and
Zeming Lin and
Natalia Gimelshein and
Luca Antiga and
Alban Desmaison and
Andreas K{\"{o}}pf and
Edward Yang and
Zachary DeVito and
Martin Raison and
Alykhan Tejani and
Sasank Chilamkurthy and
Benoit Steiner and
Lu Fang and
Junjie Bai and
Soumith Chintala},
    bibsource = {dblp computer science bibliography, https://dblp.org},
    biburl = {https://dblp.org/rec/conf/nips/PaszkeGMLBCKLGA19.bib},
    booktitle = {Advances in Neural Information Processing Systems 32: Annual Conference
on Neural Information Processing Systems 2019, NeurIPS 2019, December
8-14, 2019, Vancouver, BC, Canada},
    editor = {Hanna M. Wallach and
Hugo Larochelle and
Alina Beygelzimer and
Florence d'Alch{\'{e}}{-}Buc and
Emily B. Fox and
Roman Garnett},
    pages = {8024--8035},
    timestamp = {Thu, 21 Jan 2021 00:00:00 +0100},
    title = {{PyTorch}: An Imperative Style, High-Performance Deep Learning Library},
    url = {https://proceedings.neurips.cc/paper/2019/hash/bdbca288fee7f92f2bfa9f7012727740-Abstract.html},
    year = {2019}
}

@inproceedings{barzilay2008modeling,
    address = {Ann Arbor, Michigan},
    author = {Barzilay, Regina  and
Lapata, Mirella},
    booktitle = {Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics ({ACL}{'}05)},
    doi = {10.3115/1219840.1219858},
    pages = {141--148},
    publisher = {Association for Computational Linguistics},
    title = {Modeling Local Coherence: An Entity-Based Approach},
    url = {https://aclanthology.org/P05-1018},
    year = {2005}
}

@inproceedings{Saunshi2020AME,
    author = {Nikunj Saunshi and
Sadhika Malladi and
Sanjeev Arora},
    bibsource = {dblp computer science bibliography, https://dblp.org},
    biburl = {https://dblp.org/rec/conf/iclr/SaunshiMA21.bib},
    booktitle = {9th International Conference on Learning Representations, {ICLR} 2021,
Virtual Event, Austria, May 3-7, 2021},
    publisher = {OpenReview.net},
    timestamp = {Wed, 23 Jun 2021 01:00:00 +0200},
    title = {A Mathematical Exploration of Why Language Models Help Solve Downstream
Tasks},
    url = {https://openreview.net/forum?id=vVjIW3sEc1s},
    year = {2021}
}

@article{mccarthy2007vocd,
author = {Philip M. McCarthy and Scott Jarvis},
title ={vocd: A theoretical and empirical evaluation},

journal = {Language Testing},
volume = {24},
number = {4},
pages = {459-488},
year = {2007},
doi = {10.1177/0265532207080767},

URL = { 
    
        https://doi.org/10.1177/0265532207080767
    
    

},
eprint = { 
    
        https://doi.org/10.1177/0265532207080767
    
    

}
,
    abstract = { A reliable index of lexical diversity (LD) has remained stubbornly elusive for over 60 years. Meanwhile, researchers in fields as varied as stylistics, neuropathology, language acquisition, and even forensics continue to use flawed LD indices — often ignorant that their results are questionable and in some cases potentially dangerous. Recently, an LD measurement instrument known as vocd has become the virtual tool of the LD trade. In this paper, we report both theoretical and empirical evidence that calls into question the rationale for vocd and also indicates that its reliability is not optimal. Although our evidence shows that vocd's output (D) is a relatively robust indicator of the aggregate probabilities of word occurrences in a text, we show that these probabilities — and thus also D — are affected by text length. Malvern, Richards, Chipere and Durán (2004) acknowledge that D (as calculated by vocd's default method) can be affected by text length, but claim that the effects are not significant for the ranges of text lengths with which they are concerned. In this paper, we explain why D is affected by text length, and demonstrate with an extensive empirical analysis that the effects of text length are significant over certain ranges, which we identify. }
}

@techreport{Kincaid1975DerivationON,
    author = {Peter Kincaid and Robert P. Fishburne and Richard L. Rogers and Brad S. Chissom},
    title = {Derivation of New Readability Formulas (Automated Readability Index, Fog Count and Flesch Reading Ease Formula) for Navy Enlisted Personnel},
    institution = {Naval Technical Training Command Millington TN Research Branch},
    year = {1975}
}

@book{Flesch1979HowTW,
  title={How to write plain English: a book for lawyers and consumers},
  author={Rudolf Franz Flesch},
  year={1979},
  url={https://api.semanticscholar.org/CorpusID:107137765},
  publisher={University of Canterbury}
}

@book{o1966gobbledygook,
  title={Gobbledygook Has Gotta Go},
  author={O'Hayre, John},
  lccn={66061453},
  url={https://books.google.ae/books?id=1yTeNg9bxGUC},
  year={1966},
  publisher={U.S. Department of the Interior, Bureau of Land Management}
}

@InProceedings{wang-EtAl:2024:SemEval20245,

author = {Wang, Yuxia and Mansurov, Jonibek and Ivanov, Petar and su, jinyan and Shelmanov, Artem and Tsvigun, Akim and Mohammed Afzal, Osama and Mahmoud, Tarek and Puccetti, Giovanni and Arnold, Thomas and Whitehouse, Chenxi and Aji, Alham Fikri and Habash, Nizar and Gurevych, Iryna and Nakov, Preslav},

title = {SemEval-2024 Task 8: Multidomain, Multimodel and Multilingual Machine-Generated Text Detection},

booktitle = {Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)},

month = {June},

year = {2024},

address = {Mexico City, Mexico},

publisher = {Association for Computational Linguistics},

pages = {2041--2063},

abstract = {We present the results and the main findings of SemEval-2024 Task 8: Multigenerator, Multidomain, and Multilingual Machine-Generated Text Detection. The task featured three subtasks. Subtask A is a binary classification task determining whether a text is written by a human or generated by a machine. This subtask has two tracks: a monolingual track focused solely on English texts and a multilingual track. Subtask B is to detect the exact source of a text, discerning whether it is written by a human or generated by a specific LLM. Subtask C aims to identify the changing point within a text, at which the authorship transitions from human to machine. The task attracted a large number of participants: subtask A monolingual (126), subtask A multilingual (59), subtask B (70), and subtask C (30). In this paper, we present the task, analyze the results, and discuss the system submissions and the methods they used. For all subtasks, the best systems used LL! Ms.},

url = {https://aclanthology.org/2024.semeval2024-1.275}

}

@misc{llms-a-survey,
      title={Large Language Models: A Survey}, 
      author={Shervin Minaee and Tomas Mikolov and Narjes Nikzad and Meysam Chenaghlu and Richard Socher and Xavier Amatriain and Jianfeng Gao},
      year={2024},
      eprint={2402.06196},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@article{e5c6a2f3-a448-300c-922c-4e2ddd10f19a,
 ISSN = {15554023},
 URL = {http://www.jstor.org/stable/1473169},
 author = {Edgar Dale and Jeanne S. Chall},
 journal = {Educational Research Bulletin},
 number = {1},
 pages = {11--28},
 publisher = {Taylor & Francis, Ltd.},
 title = {A Formula for Predicting Readability},
 urldate = {2024-02-17},
 volume = {27},
 year = {1948}
}

@article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
          and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
          and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
          Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}

