Monotonicity Reasoning in the Age of Neural Foundation Models
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AA6U8QEBR" target="_blank" >RIV/00216208:11320/25:A6U8QEBR - isvavai.cz</a>
Result on the web
<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176774817&doi=10.1007%2fs10849-023-09411-3&partnerID=40&md5=f74a55cd216229a0774989c320aca8ca" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176774817&doi=10.1007%2fs10849-023-09411-3&partnerID=40&md5=f74a55cd216229a0774989c320aca8ca</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s10849-023-09411-3" target="_blank" >10.1007/s10849-023-09411-3</a>
Alternative languages
Result language
angličtina
Original language name
Monotonicity Reasoning in the Age of Neural Foundation Models
Original language description
The recent advance of large language models (LLMs) demonstrates that these large-scale foundation models achieve remarkable capabilities across a wide range of language tasks and domains. The success of the statistical learning approach challenges our understanding of traditional symbolic and logical reasoning. The first part of this paper summarizes several works concerning the progress of monotonicity reasoning through neural networks and deep learning. We demonstrate different methods for solving the monotonicity reasoning task using neural and symbolic approaches and also discuss their advantages and limitations. The second part of this paper focuses on analyzing the capability of large-scale general-purpose language models to reason with monotonicity. © The Author(s) 2023.
Czech name
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Czech description
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Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS 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)
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
Name of the periodical
Journal of Logic, Language and Information
ISSN
0925-8531
e-ISSN
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Volume of the periodical
33
Issue of the periodical within the volume
1
Country of publishing house
US - UNITED STATES
Number of pages
20
Pages from-to
49-68
UT code for WoS article
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EID of the result in the Scopus database
2-s2.0-85176774817