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The rapid adoption of generative language models has brought about substantial advancements in digital communication, while simultaneously raising concerns regarding the potential misuse of AI-generated content.
Vocabulary size and use: Lexical richness in l2 written production
Laufer, B. & Nation, P · 1995
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Short texts, best-fitting curves and new measures of lexical diversity
Jarvis, S · 2002
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Lexical richness in the spontaneous speech of bilinguals
Daller, H., Van Hout, R. & Treffers-Daller, J · 2003
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Syntactic complexity measures and their relationship to l2 proficiency: A research synthesis of college-level l2 writing
Ortega, L · 2003
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A corpus-based evaluation of syntactic complexity measures as indices of college-level esl writers’ language development
Lu, X · 2011
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Should we use characteristics of conversation to measure grammatical complexity in l2 writing development?
Biber, D., Gray, B. & Poonpon, K · 2011
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Does writing development equal writing quality? a computational investigation of syntactic complexity in l2 learners
Crossley, S. A. & McNamara, D. S · 2014
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GPT-2: 1.5B release
OpenAI · 2019
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Automatic detection of generated text is easiest when humans are fooled
Ippolito, D., Duckworth, D., Callison-Burch, C. & Eck, D · 2019
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Release strategies and the social impacts of language models
Solaiman, I. et al · 2019
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Gltr: Statistical detection and visualization of generated text
Gehrmann, S., Strobelt, H. & Rush, A. M · 2019
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Automatic detection of machine generated text: A critical survey
Jawahar, G., Abdul-Mageed, M. & Lakshmanan, L. V · 2020
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All that’s ‘human’is not gold: Evaluating human evaluation of generated text
Clark, E. et al · 2021
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Tweepfake: About detecting deepfake tweets
Fagni, T., Falchi, F., Gambini, M., Martella, A. & Tesconi, M · 2021
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Evaluating human-language model interaction
Lee, M. et al · 2022
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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
Gao, C. A. et al · 2022
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All the news that’s fit to fabricate: Ai-generated text as a tool of media misinformation
Kreps, S., McCain, R. & Brundage, M · 2022
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Abstracts written by chatgpt fool scientists
Else, H · 2023
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Tools such as chatgpt threaten transparent science; here are our ground rules for their use
Editorial, N · 2023
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Clarification on large language model policy LLM
ICML · 2023
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DetectGPT: Zero-shot machine-generated text detection using probability curvature
Mitchell, E., Lee, Y., Khazatsky, A., Manning, C. D. & Finn, C · 2023
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Chatgpt banned from new york city public schools’ devices and networks
Rosenblatt, K · 2023
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Chatgpt for good? on opportunities and challenges of large language models for education
Kasneci, E. et al · 2023
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Heikkil"a, M · 2022
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Machine generated text: A comprehensive survey of threat models and detection methods
Crothers, E., Japkowicz, N. & Viktor, H · 2022
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Watermarking pre-trained language models with backdooring
Gu, C., Huang, C., Zheng, X., Chang, K.-W. & Hsieh, C.-J · 2022
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Chatgpt sets record for fastest-growing user base - analyst note
Hu, K · 2023
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Chatgpt hits 100 million users, google invests in ai bot and catgpt goes viral
Paris, M · 2023
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Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models
Kung, T. H. et al · 2023
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Would chat gpt3 get a wharton mba? a prediction based on its performance in the operations management course
Terwiesch, C · 2023
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The hewlett foundation: Automated essay scoring
Kaggle · 2023
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Paraphrasing evades detectors of ai-generated text, but retrieval is an effective defense
Krishna, K., Song, Y., Karpinska, M., Wieting, J. & Iyyer, M · 2023
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Can ai-generated text be reliably detected?
Sadasivan, V. S., Kumar, A., Balasubramanian, S., Wang, W. & Feizi, S · 2023
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A watermark for large language models
Kirchenbauer, J. et al · 2023
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ChatGPT-Detector-Bias: v1.0.0, DOI: 10.5281/zenodo.7893958 (2023)
Liang, W., Yuksekgonul, M., Mao, Y., Wu, E. & Zou, J · 2023
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