Generative AI and the end of corpus-assisted data-driven learning? Not so fast!

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F23%3A00139271" target="_blank" >RIV/00216224:14330/23:00139271 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1016/j.acorp.2023.100066" target="_blank" >http://dx.doi.org/10.1016/j.acorp.2023.100066</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.acorp.2023.100066" target="_blank" >10.1016/j.acorp.2023.100066</a>

Result language
angličtina
Original language name
Generative AI and the end of corpus-assisted data-driven learning? Not so fast!
Original language description
This article explores the potential advantages of corpora over generative artificial intelligence (GenAI) in understanding language patterns and usage, while also acknowledging the potential of GenAI to address some of the main shortcomings of corpus-based data-driven learning (DDL). One of the main advantages of corpora is that we know exactly the domain of texts from which the corpus data is derived, something that we cannot track from current large language models underlying applications like ChatGPT. We know the texts that make up large general corpora such as BNC2014 and BAWE, and can even extract full texts from these corpora if needed. Corpora also allow for more nuanced analysis of language patterns, including the statistics behind multi-word units and collocations, which can be difficult for GenAI to handle. However, it is important to note that GenAI has its own strengths in advancing our understanding of language-in-use that corpora, to date, have struggled with. We therefore argue that by combining corpus and GenAI approaches, language learners can gain a more comprehensive understanding of how language works in different contexts than is currently possible using only a single approach.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
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)

Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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