A Bibliometric Analysis of Large Language Models in Machine Translation: Trends and Advancements (2020-2024)
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A Bibliometric Analysis of Large Language Models in Machine Translation: Trends and Advancements (2020-2024)

Yulong Li
School of Foreign Languages, Fuyang Normal University, Fuyang, 236037, Anhui, China
Ling Jiang
School of Foreign Languages, Fuyang Normal University, Fuyang, 236037, Anhui, China
Jianjie Wang
School of Foreign Languages, Fuyang Normal University, Fuyang, 236037, Anhui, China
Fumeng Gao
School of Foreign Languages, Fuyang Normal University, Fuyang, 236037, Anhui, China
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Keywords

Machine Translation
Large Language Models
Neural Machine Translation
Bibliometric Analysis
Artificial Intelligence
Submitted : 2025-12-16
Accepted : 2025-12-31
Published : 2026-01-15

Abstract

This bibliometric analysis of 460 peer-reviewed articles (2020–2024) maps the rapid evolution of Large Language Models (LLMs) in machine translation. The study reveals a significant surge in research, driven by advances in transformer architectures and characterized by robust international collaboration. Key themes identified include pre-trained models, neural machine translation, and specialized applications in domains like healthcare, highlighting the field’s interdisciplinary nature. The findings offer valuable insights into current trends and future trajectories for LLM-driven translation.