mlsanityData to 14 Oct 2026

A Paradigm Shift: The Future of Machine Translation Lies with Large Language Models

arXiv 2305.01181 ↗First posted 2 May 2023cs.CLcited by 19

Not on the map. Why?

Abstract (arXiv)

Machine Translation (MT) has greatly advanced over the years due to the developments in deep neural networks. However, the emergence of Large Language Models (LLMs) like GPT-4 and ChatGPT is introducing a new phase in the MT domain. In this context, we believe that the future of MT is intricately tied to the capabilities of LLMs. These models not only offer vast linguistic understandings but also bring innovative methodologies, such as prompt-based techniques, that have the potential to further elevate MT. In this paper, we provide an overview of the significant enhancements in MT that are influenced by LLMs and advocate for their pivotal role in upcoming MT research and implementations. We highlight several new MT directions, emphasizing the benefits of LLMs in scenarios such as Long-Document Translation, Stylized Translation, and Interactive Translation. Additionally, we address the important concern of privacy in LLM-driven MT and suggest essential privacy-preserving strategies. By showcasing practical instances, we aim to demonstrate the advantages that LLMs offer, particularly in tasks like translating extended documents. We conclude by emphasizing the critical role of LLMs in guiding the future evolution of MT and offer a roadmap for future exploration in the sector.

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). Builds on 35 of its 83 references that are in the map. First posted on arXiv 2 May 2023.

Builds on

35 of 83 references.

References · 35

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  4. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,548
  5. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
  6. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,780
  7. 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,276
  8. 2023A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivitycited by 894
  9. 2023Large Language Models Are State-of-the-Art Evaluators of Translation Qualitycited by 420
  10. 2017Six Challenges for Neural Machine Translationcited by 335
  11. 2022Prompting PaLM for Translation: Assessing Strategies and Performancecited by 145
  12. 2023LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructionscited by 124
23 more
  1. 2019Neural Machine Translation: A Review and Surveycited by 100
  2. 2021Survey of Low-Resource Machine Translationcited by 92
  3. 2019Training Neural Machine Translation To Apply Terminology Constraintscited by 73
  4. 2018Unsupervised Text Style Transfer using Language Models as Discriminatorscited by 70
  5. 2021Neural Machine Translation with Monolingual Translation Memorycited by 61
  6. 2021BlonDe: An Automatic Evaluation Metric for Document-level Machine Translationcited by 58
  7. 2020A Survey of Deep Learning Techniques for Neural Machine Translationcited by 50
  8. 2017Exploiting Cross-Sentence Context for Neural Machine Translationcited by 48
  9. 2016Personalized Machine Translation: Preserving Original Author Traitscited by 47
  10. 2017A Challenge Set Approach to Evaluating Machine Translationcited by 37
  11. 2022Achieving Reliable Human Assessment of Open-Domain Dialogue Systemscited by 31
  12. 2019Multimodal Machine Translation through Visuals and Speechcited by 29
  13. 2016SYSTRAN's Pure Neural Machine Translation Systemscited by 24
  14. 2021Uncertainty-Aware Balancing for Multilingual and Multi-Domain Neural Machine Translation Trainingcited by 19
  15. 2019Controlling Neural Machine Translation Formality with Synthetic Supervisioncited by 18
  16. 2023Document Flattening: Beyond Concatenating Context for Document-Level Neural Machine Translationcited by 15
  17. 2018Translating Pro-Drop Languages with Reconstruction Modelscited by 14
  18. 2022Extrinsic Evaluation of Machine Translation Metricscited by 14
  19. 2022Tencent AI Lab - Shanghai Jiao Tong University Low-Resource Translation System for the WMT22 Translation Taskcited by 8
  20. 2021Multilingual Document-Level Translation Enables Zero-Shot Transfer From Sentences to Documentscited by 7
  21. 2022Controlling Styles in Neural Machine Translation with Activation Promptcited by 7
  22. 2022Bilingual Synchronization: Restoring Translational Relationships with Editing Operationscited by 2
  23. 2023Does Prompt-Tuning Language Model Ensure Privacy?cited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 9; 2024: 4; 2025: 3; 2026: 3.

19 papers.

Sort:

By area:

  1. 2023A Survey on Evaluation of Large Language Modelscited by 1,242
  2. 2023Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategiescited by 238
  3. 2023Macaw-LLM: Multi-Modal Language Modeling with Image, Audio, Video, and Text Integrationcited by 172
  4. 2023Exploring Human-Like Translation Strategy with Large Language Modelscited by 44
  5. 2023Findings of the WMT 2023 Shared Task on Discourse-Level Literary Translation: A Fresh Orb in the Cosmos of LLMscited by 25
  6. 2023FFT: Towards Harmlessness Evaluation and Analysis for LLMs with Factuality, Fairness, Toxicitycited by 25
  7. 2023Beyond Static Datasets: A Deep Interaction Approach to LLM Evaluationcited by 24
  8. 2024How Much Data is Enough Data? Fine-Tuning Large Language Models for In-House Translation: Performance Evaluation Across Multiple Dataset Sizescited by 17
  9. 2024PHAnToM: Persona-based Prompting Has An Effect on Theory-of-Mind Reasoning in Large Language Modelscited by 16
  10. 2023A Survey on Zero Pronoun Translationcited by 9
  11. 2024Large Language Models "Ad Referendum": How Good Are They at Machine Translation in the Legal Domain?cited by 7
  12. 2025Hala Technical Report: Building Arabic-Centric Instruction & Translation Models at Scalecited by 5
7 more
  1. 2025Test-Time Scaling of Reasoning Models for Machine Translationcited by 5
  2. 2023Revisiting Non-Autoregressive Translation at Scalecited by 2
  3. 2024Bangla AI: A Framework for Machine Translation Utilizing Large Language Models for Ethnic Mediacited by 1
  4. 2026Dual-Metric Evaluation of Social Bias in Large Language Models: Evidence from an Underrepresented Nepali Cultural Contextcited by 1
  5. 2025Stereo-LiDAR Fusion by Semi-Global Matching With Discrete Disparity-Matching Cost and Semidensificationcited by 0
  6. 2026Mix-MoE: Improving Multilingual Machine Translation of Large Language Models through Mixed MoEscited by 0
  7. 2026All In Good Time: Causality-Aware Framework for LLM-Based Simultaneous Speech-to-Speech Translationcited by 0