Large language models and political science
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A377THBJ9" target="_blank" >RIV/00216208:11320/23:377THBJ9 - isvavai.cz</a>
Result on the web
<a href="https://www.frontiersin.org/articles/10.3389/fpos.2023.1257092/full" target="_blank" >https://www.frontiersin.org/articles/10.3389/fpos.2023.1257092/full</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3389/fpos.2023.1257092" target="_blank" >10.3389/fpos.2023.1257092</a>
Alternative languages
Result language
ruština
Original language name
Large language models and political science
Original language description
"Large Language Models (LLMs) are a type of artificial intelligence that uses information from very large datasets to model the use of language and generate content. While LLMs like GPT-3 have been used widely in many applications, the recent public release of OpenAI's ChatGPT has opened more debate about the potential uses and abuses of LLMs. In this paper, we provide a brief introduction to LLMs and discuss their potential application in political science and political methodology. We use two examples of LLMs from our recent research to illustrate how LLMs open new areas of research. We conclude with a discussion of how researchers can use LLMs in their work, and issues that researchers need to be aware of regarding using LLMs in political science and political methodology."
Czech name
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Czech description
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Classification
Type
J<sub>ost</sub> - Miscellaneous article in a specialist periodical
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
"Frontiers in Political Science"
ISSN
2673-3145
e-ISSN
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Volume of the periodical
5
Issue of the periodical within the volume
2023-10-16
Country of publishing house
RU - RUSSIAN FEDERATION
Number of pages
12
Pages from-to
1-12
UT code for WoS article
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EID of the result in the Scopus database
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