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Barbara Plank

As seen in: Color Research & Application, Nature, Wiley, arXiv, The Astronomical Journal
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Articles

A reporting checklist for large language models in behavioural science

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By Stefan Feuerriegel, Christopher Barrie, M. J. Crockett, Laura K. Globig, Killian McLoughlin, Dan-Mircea Mirea, Arthur Spirling, Diyi Yang, Tim Althoff, Maria Antoniak, Lisa P. Argyle, Ashwini Ashokkumar, Mohammad Atari, Hannah Bailey, Kevin Bauer, Umang Bhatt, Yidong Chai, Tanmoy Chakraborty Verified, Yanto Chandra, Huimin Chen, Hal Daumé III, Gianmarco De Francisci Morales, Morteza Dehghani, Danica Dillion, Johannes C. Eichstaedt, Kerstin Förster, Dominique Geissler, Kurt Gray, Thomas L. Griffiths, Hartmann Jochen, Oliver Hauser, James He, Rahul Hemrajani, Felix Holzmeister, Angel Hsing-Chi Hwang, Tiancheng Hu, Anna Ivanova, Nils Köbis, Yara Kyrychenko, Himabindu Lakkaraju, Jia Liu, Abdurahman Maarouf, Sebastian Maier, Lennart Meincke, Rada Mihalcea, Brent Mittelstadt, Saif M. Mohammad, Mor Naaman, Oded Netzer, Alice Oh, Desmond C. Ong, Francesco Pierri, Barbara Plank, Iyad Rahwan, Talal Rahwan, Pooja Rao, Claire Robertson, David Rothschild, Matthew Salganik, Eric Schulz, Chirag Shah, Yash Shrestha, Ekaterina Shutova, Alexandra Siegel, Almog Simchon, Huan Sun, Malte Toetzke, Jay Van Bavel, Michelle Vaccaro, Jennifer Vaughan, Effy Vayena, Pedro O. S. Vaz-de-Melo, Briana Vecchione, Angelina Wang, Robert West, Robb Willer, Dirk U. Wulff, Renwen Zhang, Simone Zhang, Steve Rathje, Manoel Horta Ribeiro
| Nature Verified
Large language models offer new opportunities for behavioural science, but their rapid evolution poses challenges for research rigour. We introduce a consensus-based reporting checklist to improve transparency, reproducibility and ethical accountability of large-language-model-based research in the behavioural sciences. You have full access to this article via your institution.
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Computer Science > Computation and Language

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By Beiduo Chen, Barbara Plank, Gizem Gezici
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Open in Who Shared View Duplicates (657) Issue with article?

Computer Science > Computation and Language

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By Beiduo Chen, Philipp Mondorf, Barbara Plank
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Open in Who Shared View Duplicates (693) Issue with article?
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