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AI-generated text is proliferating across domains, from creative writing and journalism to marketing content and scientific articles.
Cognitive processes in revision
John R Hayes, Linda Flower, Karen A Schriver, James Stratman, Linda Carey, et al · 1987
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The rise of writing: Redefining mass literacy
Deborah Brandt · 2014
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Deep reinforcement learning from human preferences
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei · 2017
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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
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Measuring association between labels and free-text rationales
Sarah Wiegreffe, Ana Marasović, and Noah A Smith · 2020
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Training verifiers to solve math word problems, 2021
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al · 2021
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Peter Hase and Mohit Bansal · 2021
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Constitutional ai: Harmlessness from ai feedback
Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al · 2022
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Sparks: Inspiration for science writing using language models
Katy Ilonka Gero, Vivian Liu, and Lydia Chilton · 2022
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Creative writing with an ai-powered writing assistant: Perspectives from professional writers
Daphne Ippolito, Ann Yuan, Andy Coenen, and Sehmon Burnam · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
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Ai chains: Transparent and controllable human-ai interaction by chaining large language model prompts
Tongshuang Wu, Michael Terry, and Carrie Jun Cai · 2022
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Wordcraft: story writing with large language models
Ann Yuan, Andy Coenen, Emily Reif, and Daphne Ippolito · 2022
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Open problems and fundamental limitations of reinforcement learning from human feedback
Stephen Casper, Xander Davies, Claudia Shi, Thomas Krendl Gilbert, Jérémy Scheurer, Javier Rando, Rachel Freedman, Tomasz Korbak, David Lindner, Pedro Freire, et al · 2023
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Qlora: Efficient finetuning of quantized llms
Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer · 2023
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Beyond the chat: Executable and verifiable text-editing with llms
Philippe Laban, Jesse Vig, Marti A Hearst, Caiming Xiong, and Chien-Sheng Wu · 2023
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The alignment ceiling: Objective mismatch in reinforcement learning from human feedback
Nathan Lambert and Roberto Calandra · 2023
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Rlaif vs. rlhf: Scaling reinforcement learning from human feedback with ai feedback
Harrison Lee, Samrat Phatale, Hassan Mansoor, Thomas Mesnard, Johan Ferret, Kellie Lu, Colton Bishop, Ethan Hall, Victor Carbune, Abhinav Rastogi, et al · 2023
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Self-alignment with instruction backtranslation
Xian Li, Ping Yu, Chunting Zhou, Timo Schick, Omer Levy, Luke Zettlemoyer, Jason Weston, and Mike Lewis · 2023
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Let’s verify step by step
Hunter Lightman, Vineet Kosaraju, Yuri Burda, Harrison Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe · 2023
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Explanation-based finetuning makes models more robust to spurious cues
Josh Magnus Ludan, Yixuan Meng, Tai Nguyen, Saurabh Shah, Qing Lyu, Marianna Apidianaki, and Chris Callison-Burch · 2023
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Co-writing screenplays and theatre scripts with language models: Evaluation by industry professionals
Piotr Mirowski, Kory W. Mathewson, Jaylen Pittman, and Richard Evans · 2023
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Does writing with language models reduce content diversity?
Vishakh Padmakumar and He He · 2023
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Pytorch fsdp: experiences on scaling fully sharded data parallel
Yanli Zhao, Andrew Gu, Rohan Varma, Liang Luo, Chien-Chin Huang, Min Xu, Less Wright, Hamid Shojanazeri, Myle Ott, Sam Shleifer, et al · 2023
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Lmsys-chat-1m: A large-scale real-world llm conversation dataset
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Tianle Li, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zhuohan Li, Zi Lin, Eric P Xing, et al · 2023
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Homogenization effects of large language models on human creative ideation
Llm evaluators recognize and favor their own generations
Arjun Panickssery, Samuel Bowman, and Shi Feng · 2024
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Detection and measurement of syntactic templates in generated text
Chantal Shaib, Yanai Elazar, Junyi Jessy Li, and Byron C Wallace · 2024
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Scaling llm test-time compute optimally can be more effective than scaling model parameters
Charlie Snell, Jaehoon Lee, Kelvin Xu, and Aviral Kumar · 2024
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Humanizing the machine: Proxy attacks to mislead llm detectors
Tianchun Wang, Yuanzhou Chen, Zichuan Liu, Zhanwen Chen, Haifeng Chen, Xiang Zhang, and Wei Cheng · 2024
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Barrett R Anderson, Jash Hemant Shah, and Max Kreminski · 2024
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Inference-aware fine-tuning for best-of-n sampling in large language models
Yinlam Chow, Guy Tennenholtz, Izzeddin Gur, Vincent Zhuang, Bo Dai, Sridhar Thiagarajan, Craig Boutilier, Rishabh Agarwal, Aviral Kumar, and Aleksandra Faust · 2024
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Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al · 2024
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Technical report on the pangram ai-generated text classifier
Bradley Emi and Max Spero · 2024
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Impact of preference noise on the alignment performance of generative language models
Yang Gao, Dana Alon, and Donald Metzler · 2024
Cited alongside, same era.
V-star: Training verifiers for self-taught reasoners
Arian Hosseini, Xingdi Yuan, Nikolay Malkin, Aaron Courville, Alessandro Sordoni, and Rishabh Agarwal · 2024
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Delving into chatgpt usage in academic writing through excess vocabulary
Dmitry Kobak, Rita González-Márquez, Emőke-Ágnes Horvát, and Jan Lause · 2024
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Rewardbench: Evaluating reward models for language modeling
Nathan Lambert, Valentina Pyatkin, Jacob Morrison, LJ Miranda, Bill Yuchen Lin, Khyathi Chandu, Nouha Dziri, Sachin Kumar, Tom Zick, Yejin Choi, et al · 2024
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Benjamin Warner, Antoine Chaffin, Benjamin Clavié, Orion Weller, Oskar Hallström, Said Taghadouini, Alexis Gallagher, Raja Biswas, Faisal Ladhak, Tom Aarsen, et al · 2024
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Yangzhen Wu, Zhiqing Sun, Shanda Li, Sean Welleck, and Yiming Yang · 2024
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Literary voice reproduction study mfa writers vs. llms in authorial style
Anonymous · 2025
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Writing as a testbed for open ended agents, 2025
Sian Gooding, Lucia Lopez-Rivilla, and Edward Grefenstette · 2025
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Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning
Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al · 2025
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The internet’s ai slop problem is only going to get worse
John Herrman · 2025
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Test-time computing: from system-1 thinking to system-2 thinking
Yixin Ji, Juntao Li, Hai Ye, Kaixin Wu, Jia Xu, Linjian Mo, and Min Zhang · 2025
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Confessions of an ai clickbait kingpin
Kate Knibbs · 2025
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Scammy ai-generated books are flooding amazon
Kate Knibbs · 2025
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Ai slop is flooding medium
Kate Knibbs · 2025
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Some of substack’s biggest newsletters rely on ai writing tools
Kate Knibbs · 2025
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Poor alignment and steerability of large language models: Evidence from college admission essays
Jinsook Lee, A. J. Alvero, Thorsten Joachims, and René F. Kizilcec · 2025
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The widespread adoption of large language model-assisted writing across society
Weixin Liang, Yaohui Zhang, Mihai Codreanu, Jiayu Wang, Hancheng Cao, and James Zou · 2025
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Zhexiong Liu, Diane Litman, Elaine Wang, Tianwen Li, Mason Gobat, Lindsay Clare Matsumura, and Richard Correnti · 2025
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Introducing openai o1 preview
OpenAI · 2025
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Jenna Russell, Marzena Karpinska, and Mohit Iyyer · 2025
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