mlsanityData to 7 Oct 2026

Using natural language prompts for machine translation

arXiv 2202.11822 ↗First posted 23 Feb 2022cs.CLcited by 27

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Abstract (arXiv)

We explore the use of natural language prompts for controlling various aspects of the outputs generated by machine translation models. We demonstrate that natural language prompts allow us to influence properties like formality or specific dialect of the output. We show that using language names to control the output language of multilingual translation models enables positive transfer for unseen language pairs. This unlocks the ability to translate into languages not seen during fine-tuning by using their English names. We investigate how scale, number of pre-training steps, number of languages in fine-tuning, and language similarity affect this phenomenon.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). Builds on 32 of its 45 references that are in the map. First posted on arXiv 23 Feb 2022.

Builds on

32 of 45 references.

References · 32

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  4. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  5. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
  6. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
  7. 2014Sequence to Sequence Learning with Neural Networkscited by 2,974
  8. 2020mT5: A massively multilingual pre-trained text-to-text transformercited by 1,813
  9. 2018A Call for Clarity in Reporting BLEU Scorescited by 1,790
  10. 2021Multitask Prompted Training Enables Zero-Shot Task Generalizationcited by 1,462
  11. 2020Multilingual Denoising Pre-training for Neural Machine Translationcited by 1,028
  12. 2018Adafactor: Adaptive Learning Rates with Sublinear Memory Costcited by 896
20 more
  1. 2016Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translationcited by 467
  2. 2019MASS: Masked Sequence to Sequence Pre-training for Language Generationcited by 345
  3. 2020Multilingual Translation with Extensible Multilingual Pretraining and Finetuningcited by 287
  4. 2020Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translationcited by 277
  5. 2019Massively Multilingual Neural Machine Translation in the Wild: Findings and Challengescited by 253
  6. 2021Muppet: Massive Multi-task Representations with Pre-Finetuningcited by 192
  7. 2021ExT5: Towards Extreme Multi-Task Scaling for Transfer Learningcited by 161
  8. 2021Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languagescited by 133
  9. 2018When and Why are Pre-trained Word Embeddings Useful for Neural Machine Translation?cited by 125
  10. 2016Multi-Way, Multilingual Neural Machine Translation with a Shared Attention Mechanismcited by 117
  11. 2015Ask Me Anything: Dynamic Memory Networks for Natural Language Processingcited by 116
  12. 2019Tagged Back-Translationcited by 76
  13. 2019The Missing Ingredient in Zero-Shot Neural Machine Translationcited by 59
  14. 2021Language Tags Matter for Zero-Shot Neural Machine Translationcited by 29
  15. 2018Multilingual Neural Machine Translation with Task-Specific Attentioncited by 26
  16. 2020A Multilingual View of Unsupervised Machine Translationcited by 23
  17. 2020Harnessing Multilinguality in Unsupervised Machine Translation for Rare Languagescited by 22
  18. 2020Reference Language based Unsupervised Neural Machine Translationcited by 12
  19. 2019Microsoft Research Asia's Systems for WMT19cited by 10
  20. 2018Zero-Shot Dual Machine Translationcited by 6

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Citing papers per year: 2022: 7; 2023: 11; 2024: 6; 2025: 3.

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  1. 2022Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learningcited by 886
  2. 2023Prompting Large Language Model for Machine Translation: A Case Studycited by 247
  3. 2022In-context Examples Selection for Machine Translationcited by 172
  4. 2023What Makes Good Examples for Visual In-Context Learning?cited by 150
  5. 2022Prompting PaLM for Translation: Assessing Strategies and Performancecited by 144
  6. 2023Dictionary-based Phrase-level Prompting of Large Language Models for Machine Translationcited by 74
  7. 2023Investigating the Translation Performance of a Large Multilingual Language Model: the Case of BLOOMcited by 58
  8. 2023Benchmarking Machine Translation with Cultural Awarenesscited by 53
  9. 2023Exploring Effective Factors for Improving Visual In-Context Learningcited by 43
  10. 2024TasTe: Teaching Large Language Models to Translate through Self-Reflectioncited by 27
  11. 2024DelTA: An Online Document-Level Translation Agent Based on Multi-Level Memorycited by 27
  12. 2025Evaluating o1-Like LLMs: Unlocking Reasoning for Translation through Comprehensive Analysiscited by 26
15 more
  1. 2024Scaling Laws Across Model Architectures: A Comparative Analysis of Dense and MoE Models in Large Language Modelscited by 24
  2. 2022FRMT: A Benchmark for Few-Shot Region-Aware Machine Translationcited by 18
  3. 2023Interactive-Chain-Prompting: Ambiguity Resolution for Crosslingual Conditional Generation with Interactioncited by 16
  4. 2023Chain-of-Dictionary Prompting Elicits Translation in Large Language Modelscited by 16
  5. 2023DADA: Dialect Adaptation via Dynamic Aggregation of Linguistic Rulescited by 12
  6. 2023RAMP: Retrieval and Attribute-Marking Enhanced Prompting for Attribute-Controlled Translationcited by 9
  7. 2023CODET: A Benchmark for Contrastive Dialectal Evaluation of Machine Translationcited by 9
  8. 2022Zero-shot Domain Adaptation for Neural Machine Translation with Retrieved Phrase-level Promptscited by 7
  9. 2024LLM-based Translation Inference with Iterative Bilingual Understandingcited by 6
  10. 2022DC-MBR: Distributional Cooling for Minimum Bayesian Risk Decodingcited by 4
  11. 2024Dictionary Insertion Prompting for Multilingual Reasoning on Multilingual Large Language Modelscited by 3
  12. 2025Steering Large Language Models for Machine Translation Personalizationcited by 2
  13. 2024FAME-MT Dataset: Formality Awareness Made Easy for Machine Translation Purposescited by 1
  14. 2022Changes from Classical Statistics to Modern Statistics and Data Sciencecited by 0
  15. 2025SLoW: Select Low-frequency Words! Automatic Dictionary Selection for Translation on Large Language Modelscited by 0