Adapter 与 Prompt Tuning 微调方法研究综述.

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3AVSFJJTCL" target="_blank" >RIV/00216208:11320/23:VSFJJTCL - isvavai.cz</a>

Výsledek na webu

<a href="http://cea.ceaj.org/CN/Y2023/V59/I2/12" target="_blank" >http://cea.ceaj.org/CN/Y2023/V59/I2/12</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3778/j.issn.1002-8331.2209-0025" target="_blank" >10.3778/j.issn.1002-8331.2209-0025</a>

Jazyk výsledku

chorvatština

Název v původním jazyce

Adapter 与 Prompt Tuning 微调方法研究综述.

Popis výsledku v původním jazyce

"Text mining is a branch of data mining, covering a variety of technologies, among which natural language processing technology is one of the core tools of text mining, which aims to help users obtain useful information from massive data. In recent years, the pre-training model has played an important role in promoting the research and development of natural language processing, and the fine-tuning method of the pre-training model has also become an important research field. On the basis of the relevant literature on the pre-training model fine-tuning method published in recent years, this paper reviews the current mainstream Adapter and Prompt methods. First of all, the development of natural language processing is briefly combed, and the problems and difficulties in fine-tuning of pre-training models are analyzed. Secondly, two kinds of fine-tuning methods: Adapter and Prompt, and the classic methods in the this two research directions are introduced. The advantages, disadvantages and performance are analyzed and summarized. Finally, this paper summarizes the limitations of the current fine-tuning methods of the pre-training model and discusses the future development direction."

Název v anglickém jazyce

Adapter 与 Prompt Tuning 微调方法研究综述.

Popis výsledku anglicky

"Text mining is a branch of data mining, covering a variety of technologies, among which natural language processing technology is one of the core tools of text mining, which aims to help users obtain useful information from massive data. In recent years, the pre-training model has played an important role in promoting the research and development of natural language processing, and the fine-tuning method of the pre-training model has also become an important research field. On the basis of the relevant literature on the pre-training model fine-tuning method published in recent years, this paper reviews the current mainstream Adapter and Prompt methods. First of all, the development of natural language processing is briefly combed, and the problems and difficulties in fine-tuning of pre-training models are analyzed. Secondly, two kinds of fine-tuning methods: Adapter and Prompt, and the classic methods in the this two research directions are introduced. The advantages, disadvantages and performance are analyzed and summarized. Finally, this paper summarizes the limitations of the current fine-tuning methods of the pre-training model and discusses the future development direction."

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

"Journal of Computer Engineering & Applications"

ISSN

1002-8331

e-ISSN

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Svazek periodika

59

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

HR - Chorvatská republika

Počet stran výsledku

10

Strana od-do

12-21

Kód UT WoS článku

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EID výsledku v databázi Scopus

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