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The rising popularity of large language models (LLMs) has raised concerns about machine-generated text (MGT), particularly in academic settings, where issues like plagiarism and misinformation are prevalent.
Identifying Real or Fake Articles: Towards better Language Modeling
Sameer Badaskar, Sachin Agarwal, and Shilpa Arora · 2008
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Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
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Learning without forgetting
Zhizhong Li and Derek Hoiem · 2017
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icarl: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert · 2017
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Prototypical networks for few-shot learning, 2017
Jake Snell, Kevin Swersky, and Richard S. Zemel · 2017
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Learning to learn without forgetting by maximizing transfer and minimizing interference
Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, and Gerald Tesauro · 2018
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Learning to compare: Relation network for few-shot learning
Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip HS Torr, and Timothy M Hospedales · 2018
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A closer look at few-shot classification
Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, Yu-Chiang Frank Wang, and Jia-Bin Huang · 2019
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GLTR: Statistical Detection and Visualization of Generated Text
Sebastian Gehrmann, Hendrik Strobelt, and Alexander M. Rush · 2019
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Roberta: A robustly optimized BERT pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
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Language Models are Unsupervised Multitask Learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
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Sentence-bert: Sentence embeddings using siamese bert-networks
N Reimers · 2019
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Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf · 2019
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HuggingFace’s Transformers: State-of-the-art Natural Language Processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, and Jamie Brew · 2019
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Large scale incremental learning
Yue Wu, Yinpeng Chen, Lijuan Wang, Yuancheng Ye, Zicheng Liu, Yandong Guo, and Yun Fu · 2019
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Automatic Detection of Generated Text is Easiest when Humans are Fooled
Daphne Ippolito, Daniel Duckworth, Chris Callison-Burch, and Douglas Eck · 2020
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Incremental few-shot text classification with multi-round new classes: Formulation, dataset and system
Congying Xia, Wenpeng Yin, Yihao Feng, and Philip S. Yu · 2021
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Free lunch for few-shot learning: Distribution calibration
Shuo Yang, Lu Liu, and Min Xu · 2021
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Class-incremental learning: survey and performance evaluation on image classification
Marc Masana, Xialei Liu, Bartłomiej Twardowski, Mikel Menta, Andrew D Bagdanov, and Joost Van De Weijer · 2022
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Da-Wei Zhou, Zi-Wen Cai, Han-Jia Ye, De-Chuan Zhan, and Ziwei Liu · 2023
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Llm-detectaive: a tool for fine-grained machine-generated text detection, 2024
Mervat Abassy, Kareem Elozeiri, Alexander Aziz, Minh Ngoc Ta, Raj Vardhan Tomar, Bimarsha Adhikari, Saad El Dine Ahmed, Yuxia Wang, Osama Mohammed Afzal, Zhuohan Xie, Jonibek Mansurov, Ekaterina Artemova, Vladislav Mikhailov, Rui Xing, Jiahui Geng, Hasan Iqbal, Zain Muhammad Mujahid, Tarek Mahmoud, Akim Tsvigun, Alham Fikri Aji, Artem Shelmanov, Nizar Habash, Iryna Gurevych, and Preslav Nakov · 2024
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Fast-detectgpt: Efficient zero-shot detection of machine-generated text via conditional probability curvature
Guangsheng Bao, Yanbin Zhao, Zhiyang Teng, Linyi Yang, and Yue Zhang · 2024
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Navigating the risks: A survey of security, privacy, and ethics threats in llm-based agents, 2024
Yuyou Gan, Yong Yang, Zhe Ma, Ping He, Rui Zeng, Yiming Wang, Qingming Li, Chunyi Zhou, Songze Li, Ting Wang, Yunjun Gao, Yingcai Wu, and Shouling Ji · 2024
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F. Christiano, Jan Leike, and Ryan Lowe · 2022
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Class incremental learning for intent classification with limited or no old data
Debjit Paul, Daniil Sorokin, and Judith Gaspers · 2022
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How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection
Biyang Guo, Xin Zhang, Ziyuan Wang, Minqi Jiang, Jinran Nie, Yuxuan Ding, Jianwei Yue, and Yupeng Wu · 2023
Cited alongside, same era.
MGTBench: Benchmarking Machine-Generated Text Detection
Xinlei He, Xinyue Shen, Zeyuan Chen, Michael Backes, and Yang Zhang · 2023
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RADAR: Robust AI-text detection via adversarial learning
Xiaomeng Hu, Pin-Yu Chen, and Tsung-Yi Ho · 2023
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DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature
Eric Mitchell, Yoonho Lee, Alexander Khazatsky, Christopher D. Manning, and Chelsea Finn · 2023
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OpenAI · 2023
Cited alongside, same era.
Mingmeng Geng, Caixi Chen, Yanru Wu, Dongping Chen, Yao Wan, and Pan Zhou · 2024
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Spotting LLMs with binoculars: Zero-shot detection of machine-generated text
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Zhen Tao, Yanfang Chen, Dinghao Xi, Zhiyu Li, and Wei Xu · 2024
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M4GT-Bench: Evaluation Benchmark for Black-Box Machine-Generated Text Detection
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Detectrl: Benchmarking llm-generated text detection in real-world scenarios
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Jailbreak attacks and defenses against large language models: A survey, 2024
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A survey of large language models, 2024
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Beemo: Benchmark of expert-edited machine-generated outputs, 2025
Ekaterina Artemova, Jason Lucas, Saranya Venkatraman, Jooyoung Lee, Sergei Tilga, Adaku Uchendu, and Vladislav Mikhailov · 2025
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