Machine Translation in the Era of Large Language Models:A Survey of Historical and Emerging Problems

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3AYAI4QMWL" target="_blank" >RIV/00216208:11320/26:YAI4QMWL - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2078-2489/16/9/723" target="_blank" >https://www.mdpi.com/2078-2489/16/9/723</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/info16090723" target="_blank" >10.3390/info16090723</a>

Result language

angličtina

Original language name

Machine Translation in the Era of Large Language Models:A Survey of Historical and Emerging Problems

Original language description

Historically regarded as one of the most challenging tasks presented to achieve complete artificial intelligence (AI), machine translation (MT) research has seen continuous devotion over the past decade, resulting in cutting-edge architectures for the modeling of sequential information. While the majority of statistical models traditionally relied on the idea of learning from parallel translation examples, recent research exploring self-supervised and multi-task learning methods extended the capabilities of MT models, eventually allowing the creation of general-purpose large language models (LLMs). In addition to versatility in providing translations useful across languages and domains, LLMs can in principle perform any natural language processing (NLP) task given sufficient amount of task-specific examples. While LLMs now reach a point where they can both replace and augment traditional MT models, the extent of their advantages and the ways in which they leverage translation capabilities across multilingual NLP tasks remains a wide area for exploration. In this literature survey, we present an introduction to the current position of MT research with a historical look at different modeling approaches to MT, how these might be advantageous for the solution of particular problems, and which problems are solved or remain open in regard to recent developments. We also discuss the connection of MT models leading to the development of prominent LLM architectures, how they continue to support LLM performance across different tasks by providing a means for cross-lingual knowledge transfer, and the redefinition of the task with the possibilities that LLM technology brings. © 2025 by the authors.

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

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OECD FORD branch

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

Project

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Continuities

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Publication year

2025

Confidentiality

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

Name of the periodical

Information (Switzerland)

ISSN

2078-2489

e-ISSN

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Volume of the periodical

16

Issue of the periodical within the volume

9

Country of publishing house

US - UNITED STATES

Number of pages

28

Pages from-to

723

UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-105017509121