# LexMatcher: Dictionary-centric Data Collection for LLM-based Machine Translation

arXiv: https://arxiv.org/abs/2406.01441 · HTML: https://mlsanity.com/paper/2406.01441/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 48 references that are in the map.
- First posted on arXiv 3 Jun 2024.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [LIMA: Less Is More for Alignment](https://mlsanity.com/paper/2305.11206/) (2023)
- [Improving Neural Machine Translation Models with Monolingual Data](https://mlsanity.com/paper/1511.06709/) (2015)
- [Textbooks Are All You Need II: phi-1.5 technical report](https://mlsanity.com/paper/2309.05463/) (2023)
- [How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation](https://mlsanity.com/paper/2302.09210/) (2023)
- [Few-shot Learning with Multilingual Language Models](https://mlsanity.com/paper/2112.10668/) (2021)
- [In-context Examples Selection for Machine Translation](https://mlsanity.com/paper/2212.02437/) (2022)
- [Dictionary-based Phrase-level Prompting of Large Language Models for Machine Translation](https://mlsanity.com/paper/2302.07856/) (2023)
- [TIM: Teaching Large Language Models to Translate with Comparison](https://mlsanity.com/paper/2307.04408/) (2023)
- [Denoising Neural Machine Translation Training with Trusted Data and Online Data Selection](https://mlsanity.com/paper/1809.00068/) (2018)
- [On Compositional Generalization of Neural Machine Translation](https://mlsanity.com/paper/2105.14802/) (2021)
- [Dynamic Data Selection for Neural Machine Translation](https://mlsanity.com/paper/1708.00712/) (2017)
- [Reinforcement Learning based Curriculum Optimization for Neural Machine Translation](https://mlsanity.com/paper/1903.00041/) (2019)
- [Scaling Laws for Multilingual Neural Machine Translation](https://mlsanity.com/paper/2302.09650/) (2023)
- [Revisiting the Compositional Generalization Abilities of Neural Sequence Models](https://mlsanity.com/paper/2203.07402/) (2022)
- [DEEP: DEnoising Entity Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2111.07393/) (2021)
- [Data Selection Curriculum for Neural Machine Translation](https://mlsanity.com/paper/2203.13867/) (2022)

## Built on by (most cited first, 1 of 1)

- [Bridging the Linguistic Divide: A Survey on Leveraging Large Language Models for Machine Translation](https://mlsanity.com/paper/2504.01919/) (2025)

## Abstract (arXiv)

The fine-tuning of open-source large language models (LLMs) for machine translation has recently received considerable attention, marking a shift towards data-centric research from traditional neural machine translation. However, the area of data collection for instruction fine-tuning in machine translation remains relatively underexplored. In this paper, we present LexMatcher, a simple yet effective method for data curation, the design of which is driven by the coverage of senses found in bilingual dictionaries. The construction process comprises data retrieval from an existing corpus and data augmentation that supplements the infrequent senses of polysemous words. Utilizing LLaMA2 as our base model, our approach outperforms the established baselines on the WMT2022 test sets and also exhibits remarkable performance in tasks related to word sense disambiguation and specialized terminology translation. These results underscore the effectiveness of LexMatcher in enhancing LLM-based machine translation. The code, data, and models are available at https://github.com/ARIES-LM/Lexmatcher-MT.git.