Findings of the 1st Shared Task on Multi-lingual Multi-task Information Retrieval at MRL 2023
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3AP3BYWZI6" target="_blank" >RIV/00216208:11320/23:P3BYWZI6 - isvavai.cz</a>
Result on the web
<a href="https://aclanthology.org/2023.mrl-1.24/" target="_blank" >https://aclanthology.org/2023.mrl-1.24/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.18653/v1/2023.mrl-1.24" target="_blank" >10.18653/v1/2023.mrl-1.24</a>
Alternative languages
Result language
angličtina
Original language name
Findings of the 1st Shared Task on Multi-lingual Multi-task Information Retrieval at MRL 2023
Original language description
"Large language models (LLMs) excel in language understanding and generation, especiallynin English which has ample public benchmarks for various natural language processing (NLP)ntasks."
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
2023
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 3rd Workshop on Multi-lingual Representation Learning (MRL)"
ISBN
979-8-89176-056-1
ISSN
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e-ISSN
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Number of pages
14
Pages from-to
310-323
Publisher name
ACL
Place of publication
Singapore
Event location
Singapore
Event date
Jan 1, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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