Monotonicity Reasoning in the Age of Neural Foundation Models

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Monotonicity Reasoning in the Age of Neural Foundation Models

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Monotonicity Reasoning in the Age of Neural Foundation Models

Popis výsledku anglicky

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.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

—

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Logic, Language and Information

ISSN

0925-8531

e-ISSN

—

Svazek periodika

33

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

20

Strana od-do

49-68

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85176774817