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The increasing capability and widespread usage of large language models (LLMs) highlight the desirability of automatic detection of LLM-generated text.
Real or fake? Learning to discriminate machine from human generated text
Anton Bakhtin, Sam Gross, Myle Ott, Yuntian Deng, Marc’Aurelio Ranzato, and Arthur Szlam. 2019 · 1906
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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 · 1907
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Release strategies and the social impacts of language models
Irene Solaiman, Miles Brundage, Jack Clark, Amanda Askell, Ariel Herbert-Voss, Jeff Wu, Alec Radford, Gretchen Krueger, Jong Wook Kim, Sarah Kreps, et al. 2019 · 1908
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Detecting fake content with relative entropy scoring
Thomas Lavergne, Tanguy Urvoy, and François Yvon. 2008 · 2008
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SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
Hierarchical neural story generation
Angela Fan, Mike Lewis, and Yann Dauphin. 2018 · 2018
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Don’t give me the details, just the summary! Topic-aware convolutional neural networks for extreme summarization
Shashi Narayan, Shay B. Cohen, and Mirella Lapata. 2018 · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Earlier work this paper cites.
GLTR: Statistical detection and visualization of generated text
Sebastian Gehrmann, Hendrik Strobelt, and Alexander Rush. 2019 · 2019
Earlier work this paper cites.
Unifying human and statistical evaluation for natural language generation
Tatsunori B. Hashimoto, Hugh Zhang, and Percy Liang. 2019 · 2019
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PubMedQA: A dataset for biomedical research question answering
Qiao Jin, Bhuwan Dhingra, Zhengping Liu, William Cohen, and Xinghua Lu. 2019 · 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 · 2019
Earlier work this paper cites.
Defending against neural fake news
Rowan Zellers, Ari Holtzman, Hannah Rashkin, Yonatan Bisk, Ali Farhadi, Franziska Roesner, and Yejin Choi. 2019 · 2019
Earlier work this paper cites.
Automatic detection of generated text is easiest when humans are fooled
Daphne Ippolito, Daniel Duckworth, Chris Callison-Burch, and Douglas Eck. 2020 · 2020
Earlier work this paper cites.
BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020 · 2020
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Authorship attribution for neural text generation
Adaku Uchendu, Thai Le, Kai Shu, and Dongwon Lee. 2020 · 2020
Earlier work this paper cites.
Neural deepfake detection with factual structure of text
Wanjun Zhong, Duyu Tang, Zenan Xu, Ruize Wang, Nan Duan, Ming Zhou, Jiahai Wang, and Jian Yin. 2020 · 2020
Cited alongside, same era.
GPT-Neo: Large scale autoregressive language modeling with Mesh-Tensorflow
Sid Black, Leo Gao, Phil Wang, Connor Leahy, and Stella Biderman. 2021 · 2021
Cited alongside, same era.
GPT-J-6B: A 6 billion parameter autoregressive language model
Ben Wang and Aran Komatsuzaki. 2021 · 2021
Cited alongside, same era.
BARTScore: Evaluating generated text as text generation
Weizhe Yuan, Graham Neubig, and Pengfei Liu. 2021 · 2021
Cited alongside, same era.
Beat LLMs at their own game: Zero-shot LLM-generated text detection via querying ChatGPT
Biru Zhu, Lifan Yuan, Ganqu Cui, Yangyi Chen, Chong Fu, Bingxiang He, Yangdong Deng, Zhiyuan Liu, Maosong Sun, and Ming Gu. 2023 · 2021
Cited alongside, same era.
GPT-NeoX-20B: An open-source autoregressive language model
OpenAI. 2023 · 2023
Later among the works it cites.
Academic integrity considerations of AI large language models in the post-pandemic era: ChatGPT and beyond
Mike Perkins. 2023 · 2023
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Deepfake text detection: Limitations and opportunities
Jiameng Pu, Zain Sarwar, Sifat Muhammad Abdullah, Abdullah Rehman, Yoonjin Kim, Parantapa Bhattacharya, Mobin Javed, and Bimal Viswanath. 2023 · 2023
Later among the works it cites.
DetectLLM: Leveraging log rank information for zero-shot detection of machine-generated text
Jinyan Su, Terry Zhuo, Di Wang, and Preslav Nakov. 2023 · 2023
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GPTZero: Towards detection of AI-generated text using zero-shot and supervised methods
Edward Tian and Alexander Cui. 2023 · 2023
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Sidney Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, et al. 2022 · 2022
Cited alongside, same era.
ChatGPT: Optimizing language models for dialogue
OpenAI. 2022 · 2022
Cited alongside, same era.
Cross-domain detection of GPT-2-generated technical text
Juan Diego Rodriguez, Todd Hay, David Gros, Zain Shamsi, and Ravi Srinivasan. 2022 · 2022
Cited alongside, same era.
OPT: Open pre-trained transformer language models
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al. 2022 · 2022
Cited alongside, same era.
ConDA: Contrastive domain adaptation for AI-generated text detection
Amrita Bhattacharjee, Tharindu Kumarage, Raha Moraffah, and Huan Liu. 2023 · 2023
Cited alongside, same era.
GPT-sentinel: Distinguishing human and ChatGPT generated content
Yutian Chen, Hao Kang, Vivian Zhai, Liangze Li, Rita Singh, and Bhiksha Raj. 2023 · 2023
Cited alongside, same era.
PaLM: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. 2023 · 2023
Cited alongside, same era.
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. 2023 · 2023
Later among the works it cites.
Ghostbuster: Detecting text ghostwritten by large language models
Vivek Verma, Eve Fleisig, Nicholas Tomlin, and Dan Klein. 2023 · 2023
Later among the works it cites.
SeqXGPT: Sentence-level AI-generated text detection
Pengyu Wang, Linyang Li, Ke Ren, Botian Jiang, Dong Zhang, and Xipeng Qiu. 2023a · 2023
Later among the works it cites.
LLMDet: A large language models detection tool
Kangxi Wu, Liang Pang, Huawei Shen, Xueqi Cheng, and Tat-Seng Chua. 2023 · 2023
Later among the works it cites.
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 · 2024
Closest in time.
GPTScore: Evaluate as you desire
Jinlan Fu, See-Kiong Ng, Zhengbao Jiang, and Pengfei Liu. 2024 · 2024
Closest in time.
Disinformation detection: An evolving challenge in the age of LLMs
Bohan Jiang, Zhen Tan, Ayushi Nirmal, and Huan Liu. 2024 · 2024
Closest in time.
Teach LLMs to phish: Stealing private information from language models
Ashwinee Panda, Christopher A. Choquette-Choo, Zhengming Zhang, Yaoqing Yang, and Prateek Mittal. 2024 · 2024
Closest in time.
The science of detecting LLM-generated text
Ruixiang Tang, Yu-Neng Chuang, and Xia Hu. 2024 · 2024
Closest in time.
IDEATE: Detecting AI-generated text using internal and external factual structures
Quan Wang, Licheng Zhang, Zikang Guo, and Zhendong Mao. 2024 · 2024
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DNA-GPT: Divergent n-gram analysis for training-free detection of GPT-generated text
Xianjun Yang, Wei Cheng, Yue Wu, Linda Ruth Petzold, William Yang Wang, and Haifeng Chen. 2024 · 2024
Closest in time.