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The advent of large language models (LLMs) has revolutionized the field of text generation, producing outputs that closely mimic human-like writing.
Rank analysis of incomplete block designs: I. the method of paired comparisons
Ralph Allan Bradley and Milton E Terry · 1952
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Rouge: A package for automatic evaluation of summaries
Lin Chin-Yew · 2004
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Findings of the 2016 conference on machine translation (wmt16)
Ondrej Bojar, Rajen Chatterjee, Christian Federmann, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Varvara Logacheva, Christof Monz, et al · 2016
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Hierarchical neural story generation
Angela Fan, Mike Lewis, and Yann Dauphin · 2018
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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
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Openwebtext corpus
Aaron Gokaslan and Vanya Cohen · 2019
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Pubmedqa: A dataset for biomedical research question answering
Qiao Jin, Bhuwan Dhingra, Zhengping Liu, William W Cohen, and Xinghua Lu · 2019
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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
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Bertscore: Evaluating text generation with bert
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi · 2019
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu · 2020
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Authorship attribution for neural text generation
Adaku Uchendu, Thai Le, Kai Shu, and Dongwon Lee · 2020
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Gpt-neo: Large scale autoregressive language modeling with mesh-tensorflow
Sid Black, Leo Gao, Phil Wang, Connor Leahy, and Stella Biderman · 2021
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GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model
Ben Wang and Aran Komatsuzaki · 2021
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Comparing scientific abstracts generated by chatgpt to original abstracts using an artificial intelligence output detector, plagiarism detector, and blinded human reviewers
Catherine A Gao, Frederick M Howard, Nikolay S Markov, Emma C Dyer, Siddhi Ramesh, Yuan Luo, and Alexander T Pearson · 2022
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The falcon series of open language models
Ebtesam Almazrouei, Hamza Alobeidli, Abdulaziz Alshamsi, Alessandro Cappelli, Ruxandra Cojocaru, Mérouane Debbah, Étienne Goffinet, Daniel Hesslow, Julien Launay, Quentin Malartic, et al · 2023
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Guangsheng Bao, Yanbin Zhao, Zhiyang Teng, Linyi Yang, and Yue Zhang · 2023
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Evade chatgpt detectors via a single space
Shuyang Cai and Wanyun Cui · 2023
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Parameter-efficient fine-tuning of large-scale pre-trained language models
Ning Ding, Yujia Qin, Guang Yang, Fuchao Wei, Zonghan Yang, Yusheng Su, Shengding Hu, Yulin Chen, Chi-Min Chan, Weize Chen, et al · 2023
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By chatgpt fool scientists
Holly Else · 2023
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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
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Will chatgpt g et you caught? rethinking of plagiarism detection
Mohammad Khalil and Erkan Er · 2023
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Kalpesh Krishna, Y Song, M Karpinska, J Wieting, and M Iyyer · 2023
Cited alongside, same era.
Deepfake text detection in the wild
Yafu Li, Qintong Li, Leyang Cui, Wei Bi, Longyue Wang, Linyi Yang, Shuming Shi, and Yue Zhang · 2023
Cited alongside, same era.
Check me if you can: Detecting chatgpt-generated academic writing using checkgpt
Rebel: Reinforcement learning via regressing relative rewards
Zhaolin Gao, Jonathan D Chang, Wenhao Zhan, Owen Oertell, Gokul Swamy, Kianté Brantley, Thorsten Joachims, J Andrew Bagnell, Jason D Lee, and Wen Sun · 2024
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Spotting llms with binoculars: Zero-shot detection of machine-generated text
Abhimanyu Hans, Avi Schwarzschild, Valeriia Cherepanova, Hamid Kazemi, Aniruddha Saha, Micah Goldblum, Jonas Geiping, and Tom Goldstein · 2024
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Deal: Decoding-time alignment for large language models
James Y Huang, Sailik Sengupta, Daniele Bonadiman, Yi-an Lai, Arshit Gupta, Nikolaos Pappas, Saab Mansour, Katrin Kirchhoff, and Dan Roth · 2024
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Albert Q Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, et al · 2024
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Zeyan Liu, Zijun Yao, Fengjun Li, and Bo Luo · 2023
Cited alongside, same era.
Large language models can be guided to evade ai-generated text detection
Ning Lu, Shengcai Liu, Rui He, Qi Wang, Yew-Soon Ong, and Ke Tang · 2023
Cited alongside, same era.
Chatgpt-4
OpenAI · 2023
Cited alongside, same era.
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
Cited alongside, same era.
Can ai-generated text be reliably detected?
Vinu Sankar Sadasivan, Aounon Kumar, Sriram Balasubramanian, Wenxiao Wang, and Soheil Feizi · 2023
Cited alongside, same era.
New evaluation metrics capture quality degradation due to llm watermarking
Karanpartap Singh and James Zou · 2023
Cited alongside, same era.
Detectllm: Leveraging log rank information for zero-shot detection of machine-generated text
Jinyan Su, Terry Yue Zhuo, Di Wang, and Preslav Nakov · 2023
Cited alongside, same era.
Gptzero: Towards detection of ai-generated text using zero-shot and supervised methods”, 2023
Edward Tian and Alexander Cui · 2023
Cited alongside, same era.
Watermark stealing in large language models
Nikola Jovanović, Robin Staab, and Martin Vechev · 2024
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Args: Alignment as reward-guided search
Maxim Khanov, Jirayu Burapacheep, and Yixuan Li · 2024
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Paraphrasing evades detectors of ai-generated text, but retrieval is an effective defense
Kalpesh Krishna, Yixiao Song, Marzena Karpinska, John Wieting, and Mohit Iyyer · 2024
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Introducing meta llama 3: The most capable openly available llm to date
AI Meta · 2024
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Language model detectors are easily optimized against
Charlotte Nicks, Eric Mitchell, Rafael Rafailov, Archit Sharma, Christopher D Manning, Chelsea Finn, and Stefano Ermon · 2024
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Human perception of llm-generated text content in social media environments
Kristina Radivojevic, Matthew Chou, Karla Badillo-Urquiola, and Paul Brenner · 2024
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Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn · 2024
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Red teaming language model detectors with language models
Zhouxing Shi, Yihan Wang, Fan Yin, Xiangning Chen, Kai-Wei Chang, and Cho-Jui Hsieh · 2024
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Exploring the deceptive power of llm-generated fake news: A study of real-world detection challenges
Yanshen Sun, Jianfeng He, Limeng Cui, Shuo Lei, and Chang-Tien Lu · 2024
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Intrinsic dimension estimation for robust detection of ai-generated texts
Eduard Tulchinskii, Kristian Kuznetsov, Laida Kushnareva, Daniil Cherniavskii, Sergey Nikolenko, Evgeny Burnaev, Serguei Barannikov, and Irina Piontkovskaya · 2024
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Detection of machine-generated text: Literature survey
Dmytro Valiaiev · 2024
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Bypassing llm watermarks with color-aware substitutions
Qilong Wu and Varun Chandrasekaran · 2024
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Safedecoding: Defending against jailbreak attacks via safety-aware decoding
Zhangchen Xu, Fengqing Jiang, Luyao Niu, Jinyuan Jia, Bill Yuchen Lin, and Radha Poovendran · 2024
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Tinyllama: An open-source small language model
Peiyuan Zhang, Guangtao Zeng, Tianduo Wang, and Wei Lu · 2024
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Humanizing machine-generated content: Evading ai-text detection through adversarial attack
Ying Zhou, Ben He, and Le Sun · 2024
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