Thesis Proposal: Efficient Methods for Natural Language Generation/Understanding Systems
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A10511609" target="_blank" >RIV/00216208:11320/25:10511609 - isvavai.cz</a>
Result on the web
<a href="https://aclanthology.org/2025.ijcnlp-srw.18/" target="_blank" >https://aclanthology.org/2025.ijcnlp-srw.18/</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Thesis Proposal: Efficient Methods for Natural Language Generation/Understanding Systems
Original language description
While Large Language Models (LLMs) have shown remarkable performance in various Natural Language Processing (NLP) tasks, their effectiveness seem to be heavily biased toward high-resource languages. This proposal aims to address this gap by developing efficient training strategies for low-resource languages. We propose various techniques for efficient learning in simluated low-resource settings for English. We then plan to adapt these methods for low-resource languages. We plan to experiment with both natural language generation and understanding models. We evaluate the models on similar benchmarks as the BabyLM challenge for English. For other languages, we plan to use treebanks and translation techniques to create our own silver test set to evaluate the low-resource LMs.
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
R - Projekt Ramcoveho programu EK
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
The 14th International Joint Conference on Natural Language Processing and The 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics
ISBN
979-8-89176-304-3
ISSN
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e-ISSN
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Number of pages
9
Pages from-to
209-217
Publisher name
Association for Computational Linguistics
Place of publication
Kerrville, TX, USA
Event location
Mumbai, India
Event date
Dec 20, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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