Translation Performance from the User's Perspective of Large Language Models and Neural Machine Translation Systems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3AULBVJH3Z" target="_blank" >RIV/00216208:11320/23:ULBVJH3Z - isvavai.cz</a>

Výsledek na webu

<a href="https://www.webofscience.com/wos/woscc/summary/e0b8ef34-8e6b-412a-9b8f-87607433ed44-bb92f483/relevance/1" target="_blank" >https://www.webofscience.com/wos/woscc/summary/e0b8ef34-8e6b-412a-9b8f-87607433ed44-bb92f483/relevance/1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/info14100574" target="_blank" >10.3390/info14100574</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Translation Performance from the User's Perspective of Large Language Models and Neural Machine Translation Systems

Popis výsledku v původním jazyce

"The rapid global expansion of ChatGPT, which plays a crucial role in interactive knowledge sharing and translation, underscores the importance of comparative performance assessments in artificial intelligence (AI) technology. This study concentrated on this crucial issue by exploring and contrasting the translation performances of large language models (LLMs) and neural machine translation (NMT) systems. For this aim, the APIs of Google Translate, Microsoft Translator, and OpenAI's ChatGPT were utilized, leveraging parallel corpora from the Workshop on Machine Translation (WMT) 2018 and 2020 benchmarks. By applying recognized evaluation metrics such as BLEU, chrF, and TER, a comprehensive performance analysis across a variety of language pairs, translation directions, and reference token sizes was conducted. The findings reveal that while Google Translate and Microsoft Translator generally surpass ChatGPT in terms of their BLEU, chrF, and TER scores, ChatGPT exhibits superior performance in specific language pairs. Translations from non-English to English consistently yielded better results across all three systems compared with translations from English to non-English. Significantly, an improvement in translation system performance was observed as the token size increased, hinting at the potential benefits of training models on larger token sizes."

Název v anglickém jazyce

Translation Performance from the User's Perspective of Large Language Models and Neural Machine Translation Systems

Popis výsledku anglicky

"The rapid global expansion of ChatGPT, which plays a crucial role in interactive knowledge sharing and translation, underscores the importance of comparative performance assessments in artificial intelligence (AI) technology. This study concentrated on this crucial issue by exploring and contrasting the translation performances of large language models (LLMs) and neural machine translation (NMT) systems. For this aim, the APIs of Google Translate, Microsoft Translator, and OpenAI's ChatGPT were utilized, leveraging parallel corpora from the Workshop on Machine Translation (WMT) 2018 and 2020 benchmarks. By applying recognized evaluation metrics such as BLEU, chrF, and TER, a comprehensive performance analysis across a variety of language pairs, translation directions, and reference token sizes was conducted. The findings reveal that while Google Translate and Microsoft Translator generally surpass ChatGPT in terms of their BLEU, chrF, and TER scores, ChatGPT exhibits superior performance in specific language pairs. Translations from non-English to English consistently yielded better results across all three systems compared with translations from English to non-English. Significantly, an improvement in translation system performance was observed as the token size increased, hinting at the potential benefits of training models on larger token sizes."

Druh

J_ost - Ostatní články v recenzovaných periodicích

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

2023

Kód důvěrnosti údajů

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

Název periodika

"INFORMATION"

ISSN

2078-2489

e-ISSN

—

Svazek periodika

14

Číslo periodika v rámci svazku

10

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

18

Strana od-do

574-591

Kód UT WoS článku

001090032700001

EID výsledku v databázi Scopus

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