Industrial applications of large language models
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F25%3A10257696" target="_blank" >RIV/61989100:27740/25:10257696 - isvavai.cz</a>
Result on the web
<a href="https://www.nature.com/articles/s41598-025-98483-1#article-info" target="_blank" >https://www.nature.com/articles/s41598-025-98483-1#article-info</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1038/s41598-025-98483-1" target="_blank" >10.1038/s41598-025-98483-1</a>
Alternative languages
Result language
angličtina
Original language name
Industrial applications of large language models
Original language description
Large language models (LLMs) are artificial intelligence (AI) based computational models designed to understand and generate human like text. With billions of training parameters, LLMs excel in identifying intricate language patterns, enabling remarkable performance across a variety of natural language processing (NLP) tasks. After the introduction of transformer architectures, they are impacting the industry with their text generation capabilities. LLMs play an innovative role across various industries by automating NLP tasks. In healthcare, they assist in diagnosing diseases, personalizing treatment plans, and managing patient data. LLMs provide predictive maintenance in automotive industry. LLMs provide recommendation systems, and consumer behavior analyzers. LLMs facilitates researchers and offer personalized learning experiences in education. In finance and banking, LLMs are used for fraud detection, customer service automation, and risk management. LLMs are driving significant advancements across the industries by automating tasks, improving accuracy, and providing deeper insights. Despite these advancements, LLMs face challenges such as ethical concerns, biases in training data, and significant computational resource requirements, which must be addressed to ensure impartial and sustainable deployment. This study provides a comprehensive analysis of LLMs, their evolution, and their diverse applications across industries, offering researchers valuable insights into their transformative potential and the accompanying limitations.
Czech name
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Czech description
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Classification
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
21100 - Other engineering and technologies
Result continuities
Project
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Continuities
O - Projekt operacniho programu
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Scientific Reports
ISSN
2045-2322
e-ISSN
2045-2322
Volume of the periodical
15
Issue of the periodical within the volume
1
Country of publishing house
GB - UNITED KINGDOM
Number of pages
23
Pages from-to
13755
UT code for WoS article
001472153600029
EID of the result in the Scopus database
2-s2.0-105003251970