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pro vyhledávání: '"Alexander, Rohan"'
We investigated whether large language models (LLMs) can develop data validation tests. We considered 96 conditions each for both GPT-3.5 and GPT-4, examining different prompt scenarios, learning modes, temperature settings, and roles. The prompt sce
Externí odkaz:
http://arxiv.org/abs/2310.01402
Autor:
Katz, Lindsay, Alexander, Rohan
Public knowledge of what is said in parliament is a tenet of democracy, and a critical resource for political science research. In Australia, following the British tradition, the written record of what is said in parliament is known as Hansard. While
Externí odkaz:
http://arxiv.org/abs/2304.04561
Autor:
Alexander, Rohan, Alexander, Monica
Politics and discussion in parliament is likely to be influenced by the party in power and associated election cycles. However, little is known about the extent to which these events affect discussion and how this has changed over time. We systematic
Externí odkaz:
http://arxiv.org/abs/2111.09299
Autor:
Collins, Annie, Alexander, Rohan
To examine the reproducibility of COVID-19 research, we create a dataset of pre-prints posted to arXiv, bioRxiv, and medRxiv between 28 January 2020 and 30 June 2021 that are related to COVID-19. We extract the text from these pre-prints and parse th
Externí odkaz:
http://arxiv.org/abs/2107.10724
Autor:
Alexander, Rohan, Caetano, Samantha-Jo, Chen, Haoluan, Chong, Michael, Collins, Annie, Deng, Shirley, Ehrlich, Isaac, Hodgetts, Paul, Joo, Yena, Pejcinovska, Marija, Walaa, Mariam, Wankiewicz, Matthew
We describe a series of interactive, student-developed, self-paced, modules for learning R. We detail the components of this resource, and the pedagogical underpinning. We discuss the development of this resource, and avenues for future work. Our res
Externí odkaz:
http://arxiv.org/abs/2105.09347
Sophisticated language models such as OpenAI's GPT-3 can generate hateful text that targets marginalized groups. Given this capacity, we are interested in whether large language models can be used to identify hate speech and classify text as sexist o
Externí odkaz:
http://arxiv.org/abs/2103.12407
Autor:
Chiu, Ke-Li, Alexander, Rohan
In this paper, we introduce a reproducible cleaning process for the text extracted from PDFs using n-gram models. Our approach compares the originally extracted text with the text generated from, or expected by, these models using earlier text as sti
Externí odkaz:
http://arxiv.org/abs/2101.05225
Autor:
Alexander, Rohan, Ward, Zachary
Publikováno v:
The Journal of Economic History, 2018 Sep 01. 78(3), 904-937.
Externí odkaz:
https://www.jstor.org/stable/26787132
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