# Augmenting Large Language Model Translators via Translation Memories

arXiv: https://arxiv.org/abs/2305.17367 · HTML: https://mlsanity.com/paper/2305.17367/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 33 references that are in the map.
- First posted on arXiv 27 May 2023.

## Builds on (most cited first, 16 of 16)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [LaMDA: Language Models for Dialog Applications](https://mlsanity.com/paper/2201.08239/) (2022)
- [How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation](https://mlsanity.com/paper/2302.09210/) (2023)
- [Prompting Large Language Model for Machine Translation: A Case Study](https://mlsanity.com/paper/2301.07069/) (2023)
- [Nearest Neighbor Machine Translation](https://mlsanity.com/paper/2010.00710/) (2020)
- [Facebook FAIR's WMT19 News Translation Task Submission](https://mlsanity.com/paper/1907.06616/) (2019)
- [In-context Examples Selection for Machine Translation](https://mlsanity.com/paper/2212.02437/) (2022)
- [Unsupervised Domain Clusters in Pretrained Language Models](https://mlsanity.com/paper/2004.02105/) (2020)
- [Prompting PaLM for Translation: Assessing Strategies and Performance](https://mlsanity.com/paper/2211.09102/) (2022)
- [Guiding Neural Machine Translation with Retrieved Translation Pieces](https://mlsanity.com/paper/1804.02559/) (2018)
- [Adaptive Machine Translation with Large Language Models](https://mlsanity.com/paper/2301.13294/) (2023)
- [Facebook AI WMT21 News Translation Task Submission](https://mlsanity.com/paper/2108.03265/) (2021)
- [Fast Nearest Neighbor Machine Translation](https://mlsanity.com/paper/2105.14528/) (2021)
- [DGT-TM: A freely Available Translation Memory in 22 Languages](https://mlsanity.com/paper/1309.5226/) (2013)
- [Prompting Neural Machine Translation with Translation Memories](https://mlsanity.com/paper/2301.05380/) (2023)

## Built on by (most cited first, 21 of 21)

- [Clustering and Ranking: Diversity-preserved Instruction Selection through Expert-aligned Quality Estimation](https://mlsanity.com/paper/2402.18191/) (2024)
- [In-Context Example Selection via Similarity Search Improves Low-Resource Machine Translation](https://mlsanity.com/paper/2408.00397/) (2024)
- [Contextual Refinement of Translations: Large Language Models for Sentence and Document-Level Post-Editing](https://mlsanity.com/paper/2310.14855/) (2023)
- [How Much Data is Enough Data? Fine-Tuning Large Language Models for In-House Translation: Performance Evaluation Across Multiple Dataset Sizes](https://mlsanity.com/paper/2409.03454/) (2024)
- [Compositional Translation: A Novel LLM-based Approach for Low-resource Machine Translation](https://mlsanity.com/paper/2503.04554/) (2025)
- [Creative and Context-Aware Translation of East Asian Idioms with GPT-4](https://mlsanity.com/paper/2410.00988/) (2024)
- [Simultaneous Machine Translation with Large Language Models](https://mlsanity.com/paper/2309.06706/) (2023)
- [A Preference-driven Paradigm for Enhanced Translation with Large Language Models](https://mlsanity.com/paper/2404.11288/) (2024)
- [Revealing the Parallel Multilingual Learning within Large Language Models](https://mlsanity.com/paper/2403.09073/) (2024)
- [Beyond Decoder-only: Large Language Models Can be Good Encoders for Machine Translation](https://mlsanity.com/paper/2503.06594/) (2025)
- [Bridging the Linguistic Divide: A Survey on Leveraging Large Language Models for Machine Translation](https://mlsanity.com/paper/2504.01919/) (2025)
- [Retrieving Examples from Memory for Retrieval Augmented Neural Machine Translation: A Systematic Comparison](https://mlsanity.com/paper/2404.02835/) (2024)
- [Enhancing Data Privacy in Large Language Models through Private Association Editing](https://mlsanity.com/paper/2406.18221/) (2024)
- [Redefining Machine Translation on Social Network Services with Large Language Models](https://mlsanity.com/paper/2504.07901/) (2025)
- [Emerging Opportunities of Using Large Language Models for Translation Between Drug Molecules and Indications](https://mlsanity.com/paper/2402.09588/) (2024)
- [Conversational SimulMT: Efficient Simultaneous Translation with Large Language Models](https://mlsanity.com/paper/2402.10552/) (2024)
- [Toolken+: Improving LLM Tool Usage with Reranking and a Reject Option](https://mlsanity.com/paper/2410.12004/) (2024)
- [Language-Specific Layer Matters: Efficient Multilingual Enhancement for Large Vision-Language Models](https://mlsanity.com/paper/2508.18381/) (2025)
- [A Context-aware Framework for Translation-mediated Conversations](https://mlsanity.com/paper/2412.04205/) (2024)
- [Convergences and Divergences between Automatic Assessment and Human Evaluation: Insights from Comparing ChatGPT-Generated Translation and Neural Machine Translation](https://mlsanity.com/paper/2401.05176/) (2024)
- [Quantifying Similarity: Text-Mining Approaches to Evaluate ChatGPT and Google Bard Content in Relation to BioMedical Literature](https://mlsanity.com/paper/2402.05116/) (2024)

## 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.