Recent Advances in Natural Language Processing via Large Pre-trained Language Models: A Survey

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AWJQZFI94" target="_blank" >RIV/00216208:11320/25:WJQZFI94 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11320/23:3DU6QJIM

Výsledek na webu

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168742564&doi=10.1145%2f3605943&partnerID=40&md5=e746136be962d7617e68f47c325facbe" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168742564&doi=10.1145%2f3605943&partnerID=40&md5=e746136be962d7617e68f47c325facbe</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3605943" target="_blank" >10.1145/3605943</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Recent Advances in Natural Language Processing via Large Pre-trained Language Models: A Survey

Popis výsledku v původním jazyce

Large, pre-trained language models (PLMs) such as BERT and GPT have drastically changed the Natural Language Processing (NLP) field. For numerous NLP tasks, approaches leveraging PLMs have achieved state-of-the-art performance. The key idea is to learn a generic, latent representation of language from a generic task once, then share it across disparate NLP tasks. Language modeling serves as the generic task, one with abundant self-supervised text available for extensive training. This article presents the key fundamental concepts of PLM architectures and a comprehensive view of the shift to PLM-driven NLP techniques. It surveys work applying the pre-training then fine-tuning, prompting, and text generation approaches. In addition, it discusses PLM limitations and suggested directions for future research. Copyright © 2023 held by the owner/author(s). Publication rights licensed to ACM.

Název v anglickém jazyce

Recent Advances in Natural Language Processing via Large Pre-trained Language Models: A Survey

Popis výsledku anglicky

Large, pre-trained language models (PLMs) such as BERT and GPT have drastically changed the Natural Language Processing (NLP) field. For numerous NLP tasks, approaches leveraging PLMs have achieved state-of-the-art performance. The key idea is to learn a generic, latent representation of language from a generic task once, then share it across disparate NLP tasks. Language modeling serves as the generic task, one with abundant self-supervised text available for extensive training. This article presents the key fundamental concepts of PLM architectures and a comprehensive view of the shift to PLM-driven NLP techniques. It surveys work applying the pre-training then fine-tuning, prompting, and text generation approaches. In addition, it discusses PLM limitations and suggested directions for future research. Copyright © 2023 held by the owner/author(s). Publication rights licensed to ACM.

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

ACM Computing Surveys

ISSN

0360-0300

e-ISSN

—

Svazek periodika

56

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

40

Strana od-do

1-40

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85168742564