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%2F23%3A3DU6QJIM" target="_blank" >RIV/00216208:11320/23:3DU6QJIM - isvavai.cz</a>

Výsledek na webu

<a href="https://dl.acm.org/doi/10.1145/3605943" target="_blank" >https://dl.acm.org/doi/10.1145/3605943</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."

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."

Druh

J_ost - Ostatní články v recenzovaných periodicích

CEP obor

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OECD FORD obor

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

Projekt

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Návaznosti

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Rok uplatnění

2023

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

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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

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EID výsledku v databázi Scopus

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