mlsanityData to 7 Oct 2026

Augmenting Large Language Model Translators via Translation Memories

arXiv 2305.17367 ↗First posted 27 May 2023cs.CLcited by 21

Not on the map. Why?

Abstract (arXiv)

Using translation memories (TMs) as prompts is a promising approach to in-context learning of machine translation models. In this work, we take a step towards prompting large language models (LLMs) with TMs and making them better translators. We find that the ability of LLMs to ``understand'' prompts is indeed helpful for making better use of TMs. Experiments show that the results of a pre-trained LLM translator can be greatly improved by using high-quality TM-based prompts. These results are even comparable to those of the state-of-the-art NMT systems which have access to large-scale in-domain bilingual data and are well tuned on the downstream tasks.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 16 of its 33 references that are in the map. First posted on arXiv 27 May 2023.

Builds on

16 of 33 references.

References · 16

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2022Emergent Abilities of Large Language Modelscited by 2,483
  3. 2022LaMDA: Language Models for Dialog Applicationscited by 1,218
  4. 2023How Good Are GPT Models at Machine Translation? A Comprehensive Evaluationcited by 344
  5. 2023Prompting Large Language Model for Machine Translation: A Case Studycited by 247
  6. 2020Nearest Neighbor Machine Translationcited by 209
  7. 2019Facebook FAIR's WMT19 News Translation Task Submissioncited by 179
  8. 2022In-context Examples Selection for Machine Translationcited by 172
  9. 2020Unsupervised Domain Clusters in Pretrained Language Modelscited by 160
  10. 2022Prompting PaLM for Translation: Assessing Strategies and Performancecited by 144
  11. 2018Guiding Neural Machine Translation with Retrieved Translation Piecescited by 63
  12. 2023Adaptive Machine Translation with Large Language Modelscited by 58
4 more
  1. 2021Facebook AI WMT21 News Translation Task Submissioncited by 57
  2. 2021Fast Nearest Neighbor Machine Translationcited by 37
  3. 2013DGT-TM: A freely Available Translation Memory in 22 Languagescited by 16
  4. 2023Prompting Neural Machine Translation with Translation Memoriescited by 2

Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 14; 2025: 5.

21 papers.

Sort:

By area:

  1. 2024Clustering and Ranking: Diversity-preserved Instruction Selection through Expert-aligned Quality Estimationcited by 53
  2. 2023Contextual Refinement of Translations: Large Language Models for Sentence and Document-Level Post-Editingcited by 21
  3. 2024In-Context Example Selection via Similarity Search Improves Low-Resource Machine Translationcited by 21
  4. 2024How Much Data is Enough Data? Fine-Tuning Large Language Models for In-House Translation: Performance Evaluation Across Multiple Dataset Sizescited by 17
  5. 2025Compositional Translation: A Novel LLM-based Approach for Low-resource Machine Translationcited by 13
  6. 2024Creative and Context-Aware Translation of East Asian Idioms with GPT-4cited by 10
  7. 2023Simultaneous Machine Translation with Large Language Modelscited by 9
  8. 2024A Preference-driven Paradigm for Enhanced Translation with Large Language Modelscited by 9
  9. 2024Revealing the Parallel Multilingual Learning within Large Language Modelscited by 8
  10. 2025Beyond Decoder-only: Large Language Models Can be Good Encoders for Machine Translationcited by 8
  11. 2025Bridging the Linguistic Divide: A Survey on Leveraging Large Language Models for Machine Translationcited by 8
  12. 2024Retrieving Examples from Memory for Retrieval Augmented Neural Machine Translation: A Systematic Comparisoncited by 7
9 more
  1. 2025Redefining Machine Translation on Social Network Services with Large Language Modelscited by 6
  2. 2024Emerging Opportunities of Using Large Language Models for Translation Between Drug Molecules and Indicationscited by 5
  3. 2024Conversational SimulMT: Efficient Simultaneous Translation with Large Language Modelscited by 4
  4. 2024Enhancing Data Privacy in Large Language Models through Private Association Editingcited by 4
  5. 2024Toolken+: Improving LLM Tool Usage with Reranking and a Reject Optioncited by 3
  6. 2025Language-Specific Layer Matters: Efficient Multilingual Enhancement for Large Vision-Language Modelscited by 3
  7. 2024A Context-aware Framework for Translation-mediated Conversationscited by 2
  8. 2024Convergences and Divergences between Automatic Assessment and Human Evaluation: Insights from Comparing ChatGPT-Generated Translation and Neural Machine Translationcited by 0
  9. 2024Quantifying Similarity: Text-Mining Approaches to Evaluate ChatGPT and Google Bard Content in Relation to BioMedical Literaturecited by 0