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Large Language Models (LLM) are already widely used to generate content for a variety of online platforms.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 1901
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Bertscore: Evaluating text generation with bert
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2019 · 1904
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Energy and policy considerations for deep learning in nlp
Emma Strubell, Ananya Ganesh, and Andrew McCallum. 2019 · 1906
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Binary codes capable of correcting deletions, insertions, and reversals
Vladimir I Levenshtein et al. 1966 · 1966
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Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
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Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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Re-evaluating the role of bleu in machine translation research
Chris Callison-Burch, Miles Osborne, and Philipp Koehn. 2006 · 2006
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Speech and language processing: An introduction to natural language processing, computational linguistics, and speech recognition
Dan Jurafsky and James H Martin. 2009 · 2009
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Confronting the challenge of quality diversity
Justin K Pugh, Lisa B Soros, Paul A Szerlip, and Kenneth O Stanley. 2015 · 2015
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Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al. 2021 · 2021
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Differentiable quality diversity
Matthew Fontaine and Stefanos Nikolaidis. 2021 · 2021
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Large language models can self-improve
Jiaxin Huang, Shixiang Shane Gu, Le Hou, Yuexin Wu, Xuezhi Wang, Hongkun Yu, and Jiawei Han. 2022 · 2022
Cited alongside, same era.
All the news that’s fit to fabricate: Ai-generated text as a tool of media misinformation
Sarah Kreps, R Miles McCain, and Miles Brundage. 2022 · 2022
Cited alongside, same era.
Choose your programming copilot: A comparison of the program synthesis performance of github copilot and genetic programming
Dominik Sobania, Martin Briesch, and Franz Rothlauf. 2022 · 2022
Cited alongside, same era.
Will we run out of data? an analysis of the limits of scaling datasets in machine learning
Pablo Villalobos, Jaime Sevilla, Lennart Heim, Tamay Besiroglu, Marius Hobbhahn, and Anson Ho. 2022 · 2022
Cited alongside, same era.
Self-instruct: Aligning language model with self generated instructions
Do you trust chatgpt?–perceived credibility of human and ai-generated content
Martin Huschens, Martin Briesch, Dominik Sobania, and Franz Rothlauf. 2023 · 2023
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Towards understanding the interplay of generative artificial intelligence and the internet
Gonzalo Martínez, Lauren Watson, Pedro Reviriego, José Alberto Hernández, Marc Juarez, and Rik Sarkar. 2023 · 2023
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Can ai-generated text be reliably detected?
Vinu Sankar Sadasivan, Aounon Kumar, Sriram Balasubramanian, Wenxiao Wang, and Soheil Feizi. 2023 · 2023
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The curse of recursion: Training on generated data makes models forget
Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Yarin Gal, Nicolas Papernot, and Ross Anderson. 2023 · 2023
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Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A Smith, Daniel Khashabi, and Hannaneh Hajishirzi. 2022 · 2022
Cited alongside, same era.
Self-consuming generative models go mad
Sina Alemohammad, Josue Casco-Rodriguez, Lorenzo Luzi, Ahmed Imtiaz Humayun, Hossein Babaei, Daniel LeJeune, Ali Siahkoohi, and Richard G Baraniuk. 2023 · 2023
Cited alongside, same era.
Education in the era of generative artificial intelligence (ai): Understanding the potential benefits of chatgpt in promoting teaching and learning
David Baidoo-Anu and Leticia Owusu Ansah. 2023 · 2023
Cited alongside, same era.
On the stability of iterative retraining of generative models on their own data
Quentin Bertrand, Avishek Joey Bose, Alexandre Duplessis, Marco Jiralerspong, and Gauthier Gidel. 2023 · 2023
Cited alongside, same era.
Large language models for software engineering: Survey and open problems
Angela Fan, Beliz Gokkaya, Mark Harman, Mitya Lyubarskiy, Shubho Sengupta, Shin Yoo, and Jie M Zhang. 2023 · 2023
Cited alongside, same era.
Repairing the cracked foundation: A survey of obstacles in evaluation practices for generated text
Sebastian Gehrmann, Elizabeth Clark, and Thibault Sellam. 2023 · 2023
Cited alongside, same era.
The curious decline of linguistic diversity: Training language models on synthetic text
Yanzhu Guo, Guokan Shang, Michalis Vazirgiannis, and Chloé Clavel. 2023 · 2023
Cited alongside, same era.
Dominik Sobania, Martin Briesch, Carol Hanna, and Justyna Petke. 2023 · 2023
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Veniamin Veselovsky, Manoel Horta Ribeiro, and Robert West. 2023 · 2023
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Denoising autoencoder genetic programming: strategies to control exploration and exploitation in search
David Wittenberg, Franz Rothlauf, and Christian Gagné. 2023 · 2023
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Exploring the limits of chatgpt for query or aspect-based text summarization
Xianjun Yang, Yan Li, Xinlu Zhang, Haifeng Chen, and Wei Cheng. 2023 · 2023
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Large language model as attributed training data generator: A tale of diversity and bias
Yue Yu, Yuchen Zhuang, Jieyu Zhang, Yu Meng, Alexander Ratner, Ranjay Krishna, Jiaming Shen, and Chao Zhang. 2023 · 2023
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Is chatgpt transforming academics’ writing style?
Mingmeng Geng and Roberto Trotta. 2024 · 2024
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Mapping the increasing use of llms in scientific papers
Weixin Liang, Yaohui Zhang, Zhengxuan Wu, Haley Lepp, Wenlong Ji, Xuandong Zhao, Hancheng Cao, Sheng Liu, Siyu He, Zhi Huang, et al. 2024 · 2024
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