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Large language models (LLMs) like ChatGPT can generate and revise text with human-level performance.
Studying the history of ideas using topic models
David Hall, Dan Jurafsky, and Christopher D Manning · 2008
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Visualizing data using t-SNE
Laurens Van der Maaten and Geoffrey Hinton · 2008
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Natural language processing with Python: analyzing text with the natural language toolkit
Steven Bird, Ewan Klein, and Edward Loper · 2009
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Scikit-learn: Machine learning in Python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al · 2011
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Universals versus historical contingencies in lexical evolution
Vladimir Bochkarev, Valery Solovyev, and Søren Wichmann · 2014
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Jane, John… Leslie? A historical method for algorithmic gender prediction
Cameron Blevins and Lincoln Mullen · 2015
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Linguistic variation and change in 250 years of English scientific writing: A data-driven approach
Yuri Bizzoni, Stefania Degaetano-Ortlieb, Peter Fankhauser, and Elke Teich · 2020
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(in) citing action to realize an equitable future
Jordan Dworkin, Perry Zurn, and Danielle S Bassett · 2020
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Retrieval-augmented generation for knowledge-intensive NLP tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al · 2020
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On the dangers of stochastic parrots: Can language models be too big?
Emily M Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell · 2021
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Domain-specific language model pretraining for biomedical natural language processing
Yu Gu, Robert Tinn, Hao Cheng, Michael Lucas, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, and Hoifung Poon · 2021
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Excess deaths associated with COVID-19 pandemic in 2020: age and sex disaggregated time series analysis in 29 high income countries
Nazrul Islam, Vladimir M Shkolnikov, Rolando J Acosta, Ilya Klimkin, Ichiro Kawachi, Rafael A Irizarry, Gianfranco Alicandro, Kamlesh Khunti, Tom Yates, Dmitri A Jdanov, et al · 2021
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Tracking excess mortality across countries during the COVID-19 pandemic with the World Mortality Dataset
Ariel Karlinsky and Dmitry Kobak · 2021
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Improving language models by retrieving from trillions of tokens
Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George Bm Van Den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, et al · 2022
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The future of academic publishing
Abubakari Ahmed, Aceil Al-Khatib, Yap Boum, Humberto Debat, Alonso Gurmendi Dunkelberg, Lisa Janicke Hinchliffe, Frith Jarrad, Adam Mastroianni, Patrick Mineault, Charlotte R Pennington, et al · 2023
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AI tools can improve equity in science
Violeta Berdejo-Espinola and Tatsuya Amano · 2023
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As scientists explore AI-written text, journals hammer out policies
Jeffrey Brainard · 2023
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Machine culture
Levin Brinkmann, Fabian Baumann, Jean-François Bonnefon, Maxime Derex, Thomas F Müller, Anne-Marie Nussberger, Agnieszka Czaplicka, Alberto Acerbi, Thomas L Griffiths, Joseph Henrich, et al · 2023
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Generative AI has a language problem
Monojit Choudhury · 2023
Cited alongside, same era.
Distinguishing academic science writing from humans or chatgpt with over 99% accuracy using off-the-shelf machine learning tools
Heather Desaire, Aleesa E Chua, Madeline Isom, Romana Jarosova, and David Hua · 2023
Cited alongside, same era.
Survey of hallucination in natural language generation
Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung · 2023
Cited alongside, same era.
Funding agencies say no to AI peer review
Jocelyn Kaiser · 2023
Cited alongside, same era.
LLMs are not ready for editorial work
Grace W Lindsay · 2023
Cited alongside, same era.
How much do language models copy from their training data? Evaluating linguistic novelty in text generation using RAVEN
R Thomas McCoy, Paul Smolensky, Tal Linzen, Jianfeng Gao, and Asli Celikyilmaz · 2023
Arslan Akram · 2024
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Large language models, social demography, and hegemony: Comparing authorship in human and synthetic text
AJ Alvero, Jinsook Lee, Alejandra Regla-Vargas, Rene Kizilec, Thorsten Joachims, and Anthony Lising Antonio · 2024
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Delving into the utilisation of ChatGPT in scientific publications in astronomy
Simone Astarita, Sandor Kruk, Jan Reerink, and Pablo Gómez · 2024
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Measuring implicit bias in explicitly unbiased large language models
Xuechunzi Bai, Angelina Wang, Ilia Sucholutsky, and Thomas L Griffiths · 2024
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Have AI-generated texts from LLM infiltrated the realm of scientific writing? A large-scale analysis of preprint platforms
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Cited alongside, same era.
To protect science, we must use LLMs as zero-shot translators
Brent Mittelstadt, Sandra Wachter, and Chris Russell · 2023
Cited alongside, same era.
The WHO estimates of excess mortality associated with the COVID-19 pandemic
William Msemburi, Ariel Karlinsky, Victoria Knutson, Serge Aleshin-Guendel, Somnath Chatterji, and Jon Wakefield · 2023
Cited alongside, same era.
AI language tools risk scientific diversity and innovation
Ryosuke Nakadai, Yo Nakawake, and Shota Shibasaki · 2023
Cited alongside, same era.
Biases in large language models: origins, inventory, and discussion
Roberto Navigli, Simone Conia, and Björn Ross · 2023
Cited alongside, same era.
Does writing with language models reduce content diversity?
Vishakh Padmakumar and He He · 2023
Cited alongside, same era.
Hindawi shuttering four journals overrun by paper mills, 2023
Retraction Watch · 2023
Cited alongside, same era.
Huzi Cheng, Bin Sheng, Aaron Lee, Varun Chaudhary, Atanas G Atanasov, Nan Liu, Yue Qiu, Tien Yin Wong, Yih-Chung Tham, and Ying-Feng Zheng · 2024
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Is ChatGPT transforming academics’ writing style?
Mingmeng Geng and Roberto Trotta · 2024
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The landscape of biomedical research
Rita González-Márquez, Luca Schmidt, Benjamin M Schmidt, Philipp Berens, and Dmitry Kobak · 2024
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ChatGPT “contamination”: estimating the prevalence of LLMs in the scholarly literature
Andrew Gray · 2024
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Use of large language models might affect our cognitive skills
Richard Heersmink · 2024
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Risks of abuse of large language models, like ChatGPT, in scientific publishing: Authorship, predatory publishing, and paper mills
Graham Kendall and Jaime A Teixeira da Silva · 2024
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FABLES: Evaluating faithfulness and content selection in book-length summarization
Yekyung Kim, Yapei Chang, Marzena Karpinska, Aparna Garimella, Varun Manjunatha, Kyle Lo, Tanya Goyal, and Mohit Iyyer · 2024
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Detecting LLM-assisted writing in scientific communication: Are we there yet?
Teddy Lazebnik and Ariel Rosenfeld · 2024
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Jialin Liu and Yi Bu · 2024
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Delving into PubMed records: Some terms in medical writing have drastically changed after the arrival of ChatGPT
Kentaro Matsui · 2024
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Analysing the impact of ChatGPT in research
Pablo Picazo-Sanchez and Lara Ortiz-Martin · 2024
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Adapted large language models can outperform medical experts in clinical text summarization
Dave Van Veen, Cara Van Uden, Louis Blankemeier, Jean-Benoit Delbrouck, Asad Aali, Christian Bluethgen, Anuj Pareek, Malgorzata Polacin, Eduardo Pontes Reis, Anna Seehofnerová, et al · 2024
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Empirical evidence of Large Language Model’s influence on human spoken communication
Hiromu Yakura, Ezequiel Lopez-Lopez, Levin Brinkmann, Ignacio Serna, Prateek Gupta, and Iyad Rahwan · 2024
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Benchmarking large language models for news summarization
Tianyi Zhang, Faisal Ladhak, Esin Durmus, Percy Liang, Kathleen McKeown, and Tatsunori B Hashimoto · 2024
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