@article{radford2019language,
  title={Language models are unsupervised multitask learners},
  author={Radford, Alec and Wu, Jeffrey and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya and others},
  journal={OpenAI blog},
  volume={1},
  number={8},
  pages={9},
  year={2019}
}

@inproceedings{NEURIPS2020_1457c0d6,
 author = {Brown, Tom and Mann, Benjamin and Ryder, Nick and Subbiah, Melanie and Kaplan, Jared D 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 and Wu, Jeffrey and Winter, Clemens and Hesse, Chris 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},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
 pages = {1877--1901},
 publisher = {Curran Associates, Inc.},
 title = {Language Models are Few-Shot Learners},
 url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf},
 volume = {33},
 year = {2020}
}

@article{ouyang2022training,
  title={Training language models to follow instructions with human feedback},
  author={Ouyang, Long and Wu, Jeffrey and Jiang, Xu and Almeida, Diogo and Wainwright, Carroll and Mishkin, Pamela and Zhang, Chong and Agarwal, Sandhini and Slama, Katarina and Ray, Alex and others},
  journal={Advances in Neural Information Processing Systems},
  volume={35},
  pages={27730--27744},
  year={2022}
}

@article{achiam2023gpt,
  title={Gpt-4 technical report},
  author={Achiam, Josh and Adler, Steven and Agarwal, Sandhini and Ahmad, Lama and Akkaya, Ilge and Aleman, Florencia Leoni and Almeida, Diogo and Altenschmidt, Janko and Altman, Sam and Anadkat, Shyamal and others},
  journal={arXiv preprint arXiv:2303.08774},
  year={2023}
}

@article{ma2023ai,
  title={AI vs. human--differentiation analysis of scientific content generation},
  author={Ma, Yongqiang and Liu, Jiawei and Yi, Fan and Cheng, Qikai and Huang, Yong and Lu, Wei and Liu, Xiaozhong},
  journal={arXiv},
  volume={2301},
  year={2023}
}

@article{lewis2020retrieval,
  title={Retrieval-augmented generation for knowledge-intensive nlp tasks},
  author={Lewis, Patrick and Perez, Ethan and Piktus, Aleksandra and Petroni, Fabio and Karpukhin, Vladimir and Goyal, Naman and K{\"u}ttler, Heinrich and Lewis, Mike and Yih, Wen-tau and Rockt{\"a}schel, Tim and others},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={9459--9474},
  year={2020}
}

@article{guo2023close,
  title={How close is chatgpt to human experts? comparison corpus, evaluation, and detection},
  author={Guo, Biyang and Zhang, Xin and Wang, Ziyuan and Jiang, Minqi and Nie, Jinran and Ding, Yuxuan and Yue, Jianwei and Wu, Yupeng},
  journal={arXiv preprint arXiv:2301.07597},
  year={2023}
}

@article{shijaku2023chatgpt,
  title={ChatGPT Generated Text Detection},
  author={Shijaku, Rexhep and Canhasi, Ercan},
  journal={Publisher: Unpublished},
  year={2023}
}

@article{zellers2019defending,
  title={Defending against neural fake news},
  author={Zellers, Rowan and Holtzman, Ari and Rashkin, Hannah and Bisk, Yonatan and Farhadi, Ali and Roesner, Franziska and Choi, Yejin},
  journal={Advances in neural information processing systems},
  volume={32},
  year={2019}
}

@article{DBLP:journals/corr/abs-1810-04805,
  author    = {Jacob Devlin and
               Ming{-}Wei Chang and
               Kenton Lee and
               Kristina Toutanova},
  title     = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language
               Understanding},
  journal   = {CoRR},
  volume    = {abs/1810.04805},
  year      = {2018},
  url       = {http://arxiv.org/abs/1810.04805},
  archivePrefix = {arXiv},
  eprint    = {1810.04805},
  timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{roberta,
  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},
  title     = {RoBERTa: {A} Robustly Optimized {BERT} Pretraining Approach},
  journal   = {CoRR},
  volume    = {abs/1907.11692},
  year      = {2019},
  url       = {http://arxiv.org/abs/1907.11692},
  archivePrefix = {arXiv},
  eprint    = {1907.11692},
  timestamp = {Thu, 01 Aug 2019 08:59:33 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1907-11692.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{garcia2022umutextstats,
  title={Umutextstats: A linguistic feature extraction tool for spanish},
  author={Garc{\'\i}a-D{\'\i}az, Jos{\'e} Antonio and Vivancos-Vicente, Pedro Jos{\'e} and Almela, Angela and Valencia-Garc{\'\i}a, Rafael},
  booktitle={Proceedings of the Thirteenth Language Resources and Evaluation Conference},
  pages={6035--6044},
  year={2022}
}


@article{bayer2022augmentationsurvey,
author = {Bayer, Markus and Kaufhold, Marc-Andr\'{e} and Reuter, Christian},
title = {A Survey on Data Augmentation for Text Classification},
year = {2022},
issue_date = {July 2023},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {55},
number = {7},
issn = {0360-0300},
url = {https://doi.org/10.1145/3544558},
doi = {10.1145/3544558},
abstract = {Data augmentation, the artificial creation of training data for machine learning by transformations, is a widely studied research field across machine learning disciplines. While it is useful for increasing a model's generalization capabilities, it can also address many other challenges and problems, from overcoming a limited amount of training data to regularizing the objective, to limiting the amount of data used to protect privacy. Based on a precise description of the goals and applications of data augmentation and a taxonomy for existing works, this survey is concerned with data augmentation methods for textual classification and aims at providing a concise and comprehensive overview for researchers and practitioners. Derived from the taxonomy, we divide more than 100 methods into 12 different groupings and give state-of-the-art references expounding which methods are highly promising by relating them to each other. Finally, research perspectives that may constitute a building block for future work are provided.},
journal = {ACM Comput. Surv.},
month = {dec},
articleno = {146},
numpages = {39},
keywords = {small data analytics, low data regimes, Data augmentation}
}
@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 = {}
}

@article{garcia2022psychographic,
  title={Psychographic traits identification based on political ideology: An author analysis study on spanish politicians’ tweets posted in 2020},
  author={Garc{\'\i}a-D{\'\i}az, Jos{\'e} Antonio and Colomo-Palacios, Ricardo and Valencia-Garc{\'\i}a, Rafael},
  journal={Future Generation Computer Systems},
  volume={130},
  pages={59--74},
  year={2022},
  publisher={Elsevier}
}

@article{garcia2023evaluating,
  title={Evaluating feature combination strategies for hate-speech detection in spanish using linguistic features and transformers},
  author={Garc{\'\i}a-D{\'\i}az, Jos{\'e} Antonio and Jim{\'e}nez-Zafra, Salud Mar{\'\i}a and Garc{\'\i}a-Cumbreras, Miguel Angel and Valencia-Garc{\'\i}a, Rafael},
  journal={Complex \& Intelligent Systems},
  volume={9},
  number={3},
  pages={2893--2914},
  year={2023},
  publisher={Springer}
}

@article{saticorpus,
  title={Compilation and evaluation of the spanish saticorpus 2021 for satire identification using linguistic features and transformers},
  author={Garc{\'\i}a-D{\'\i}az, Jos{\'e} Antonio and Valencia-Garc{\'\i}a, Rafael},
  journal={Complex \& Intelligent Systems},
  volume={8},
  number={2},
  pages={1723--1736},
  year={2022},
  publisher={Springer}
}

@article{garcia2023smart,
  title={Smart analysis of economics sentiment in Spanish based on linguistic features and transformers},
  author={Garc{\'\i}a-D{\'\i}az, Jos{\'e} Antonio and Garc{\'\i}a-S{\'a}nchez, Francisco and Valencia-Garc{\'\i}a, Rafael},
  journal={IEEE Access},
  volume={11},
  pages={14211--14224},
  year={2023},
  publisher={IEEE}
}