Evaluating large language models for the tasks of PoS tagging within the Universal Dependency framework
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3ALDFJCHAJ" target="_blank" >RIV/00216208:11320/25:LDFJCHAJ - isvavai.cz</a>
Result on the web
<a href="https://aclanthology.org/2024.propor-1.46" target="_blank" >https://aclanthology.org/2024.propor-1.46</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Evaluating large language models for the tasks of PoS tagging within the Universal Dependency framework
Original language description
Large language models (LLMs) have emerged as a valuable tool for a variety of natural lan- guage processing tasks. This study focuses on assessing the capabilities of three language models in the context of part-of-speech tagging using the Universal Dependency (UPoS) tagset in texts written in Brazilian Portuguese. Our experiments reveal that LLMs can effectively leverage prior knowledge from existing tagged datasets and can also extract linguistic structure with arbitrary labels. Furthermore, we present results indicating an accuracy of 90% in UPoS tagging for a multilingual model, while smaller monolingual models achieve an accuracy of 48%.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
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
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 16th International Conference on Computational Processing of Portuguese - Vol. 1
ISBN
979-8-89176-062-2
ISSN
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e-ISSN
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Number of pages
7
Pages from-to
454-460
Publisher name
Association for Computational Lingustics
Place of publication
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Event location
Santiago de Compostela, Galicia/Spain
Event date
Jan 1, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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