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The Uniform Information Density (UID) principle posits that humans prefer to spread information evenly during language production.
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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A mathematical theory of communication
Claude E Shannon. 1948 · 1948
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Spoken and written English corpus of Chinese learners
Qiufang Wen, Lifei Wang, and Maocheng Liang. 2005 · 2005
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Speakers optimize information density through syntactic reduction
T Florian Jaeger and Roger P Levy. 2007 · 2007
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Writeprints: A Stylometric Approach to Identity-Level Identification and Similarity Detection in Cyberspace
Ahmed Abbasi and Hsinchun Chen. 2008 · 2008
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Speaking rationally: Uniform information density as an optimal strategy for language production
Austin F Frank and T Florain Jaeger. 2008 · 2008
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Refer efficiently: Use less informative expressions for more predictable meanings
Harry Tily and Steven Piantadosi. 2009 · 2009
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Redundancy and reduction: Speakers manage syntactic information density
T Florian Jaeger. 2010 · 2010
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TOEFL11: A corpus of non-native English
Daniel Blanchard, Joel Tetreault, Derrick Higgins, Aoife Cahill, and Martin Chodorow. 2013 · 2013
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Info/information theory: Speakers choose shorter words in predictive contexts
Kyle Mahowald, Evelina Fedorenko, Steven T Piantadosi, and Edward Gibson. 2013 · 2013
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TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation
Adaku Uchendu, Zeyu Ma, Thai Le, Rui Zhang, and Dongwon Lee. 2021 · 2016
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Entropy converges between dialogue participants: Explanations from an information-theoretic perspective
Yang Xu and David Reitter. 2016 · 2016
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Uniform Information Density effects on syntactic choice in Hindi
Ayush Jain, Vishal Singh, Sidharth Ranjan, Rajakrishnan Rajkumar, and Sumeet Agarwal. 2018 · 2018
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Information density converges in dialogue: Towards an information-theoretic model
Yang Xu and David Reitter. 2018 · 2018
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GLTR: Statistical Detection and Visualization of Generated Text
Sebastian Gehrmann, Hendrik Strobelt, and Alexander M Rush. 2019 · 2019
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. 2019 · 2019
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Defending against neural fake news
Rowan Zellers, Ari Holtzman, Hannah Rashkin, Yonatan Bisk, Ali Farhadi, Franziska Roesner, and Yejin Choi. 2019 · 2019
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Automatic Detection of Machine Generated Text: A Critical Survey
Ganesh Jawahar, Muhammad Abdul-Mageed, and VS Laks Lakshmanan. 2020 · 2020
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If beam search is the answer, what was the question?
Clara Meister, Ryan Cotterell, and Tim Vieira. 2020 · 2020
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Authorship attribution for neural text generation
Adaku Uchendu, Thai Le, Kai Shu, and Dongwon Lee. 2020 · 2020
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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
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Extracting Training Data from Large Language Models
Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom B Brown, Dawn Song, Ulfar Erlingsson, et al. 2021 · 2021
Do language models plagiarize?
Jooyoung Lee, Thai Le, Jinghui Chen, and Dongwon Lee. 2023 · 2023
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Deepfake Text Detection in the Wild
Yafu Li, Qintong Li, Leyang Cui, Wei Bi, Longyue Wang, Linyi Yang, Shuming Shi, and Yue Zhang. 2023 · 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 · 2023
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OpenAI. 2023 · 2023
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Guilherme Penedo, Quentin Malartic, Daniel Hesslow, Ruxandra Cojocaru, Alessandro Cappelli, Hamza Alobeidli, Baptiste Pannier, Ebtesam Almazrouei, and Julien Launay. 2023 · 2023
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Unsupervised and distributional detection of machine-generated text
Matthias Gallé, Jos Rozen, Germán Kruszewski, and Hady Elsahar. 2021 · 2021
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Artificial Text Detection via Examining the Topology of Attention Maps
Laida Kushnareva, Daniil Cherniavskii, Vladislav Mikhailov, Ekaterina Artemova, Serguei Barannikov, Alexander Bernstein, Irina Piontkovskaya, Dmitri Piontkovski, and Evgeny Burnaev. 2021 · 2021
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Revisiting the Uniform Information Density Hypothesis
Clara Meister, Tiago Pimentel, Patrick Haller, Lena Jäger, Ryan Cotterell, and Roger Levy. 2021 · 2021
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A Cognitive Regularizer for Language Modeling
Jason Wei, Clara Meister, and Ryan Cotterell. 2021 · 2021
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Xiaoming Liu, Zhaohan Zhang, Yichen Wang, Yu Lan, and Chao Shen. 2022 · 2022
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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. 2022 · 2022
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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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Model evaluation for extreme risks
Toby Shevlane, Sebastian Farquhar, Ben Garfinkel, Mary Phuong, Jess Whittlestone, Jade Leung, Daniel Kokotajlo, Nahema Marchal, Markus Anderljung, Noam Kolt, et al. 2023 · 2023
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DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text
Jinyan Su, Terry Yue 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
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Llama: Open and efficient foundation language models
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Attribution and obfuscation of neural text authorship: A data mining perspective
Adaku Uchendu, Thai Le, and Dongwon Lee. 2023 · 2023
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How do decoding algorithms distribute information in dialogue responses?
Saranya Venkatraman, He He, and David Reitter. 2023 · 2023
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A Survey on Detection of LLMs-Generated Content
Xianjun Yang, Liangming Pan, Xuandong Zhao, Haifeng Chen, Linda Petzold, William Yang Wang, and Wei Cheng. 2023 · 2023
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AI Text Detector
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A survey of large language models
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al. 2023 · 2023
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