# Rule-Based, Neural and LLM Back-Translation: Comparative Insights from a Variant of Ladin

arXiv: https://arxiv.org/abs/2407.08819 · HTML: https://mlsanity.com/paper/2407.08819/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 34 references that are in the map.
- First posted on arXiv 11 Jul 2024.

## Builds on (most cited first, 15 of 15)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [Improving Neural Machine Translation Models with Monolingual Data](https://mlsanity.com/paper/1511.06709/) (2015)
- [Prompting Large Language Model for Machine Translation: A Case Study](https://mlsanity.com/paper/2301.07069/) (2023)
- [Massively Multilingual Neural Machine Translation](https://mlsanity.com/paper/1903.00089/) (2019)
- [Transfer Learning for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/1604.02201/) (2016)
- [The Troubling Emergence of Hallucination in Large Language Models -- An Extensive Definition, Quantification, and Prescriptive Remediations](https://mlsanity.com/paper/2310.04988/) (2023)
- [Neural Machine Translation for Low-Resource Languages: A Survey](https://mlsanity.com/paper/2106.15115/) (2021)
- [Survey of Low-Resource Machine Translation](https://mlsanity.com/paper/2109.00486/) (2021)
- [Tagged Back-Translation](https://mlsanity.com/paper/1906.06442/) (2019)
- [On The Evaluation of Machine Translation Systems Trained With Back-Translation](https://mlsanity.com/paper/1908.05204/) (2019)
- [Using Monolingual Data in Neural Machine Translation: a Systematic Study](https://mlsanity.com/paper/1903.11437/) (2019)
- [The Source-Target Domain Mismatch Problem in Machine Translation](https://mlsanity.com/paper/1909.13151/) (2019)
- [On Synthetic Data for Back Translation](https://mlsanity.com/paper/2310.13675/) (2023)

## Built on by (most cited first, 5 of 5)

- [Bridging the Linguistic Divide: A Survey on Leveraging Large Language Models for Machine Translation](https://mlsanity.com/paper/2504.01919/) (2025)
- [Compensating for Data with Reasoning: Low-Resource Machine Translation with LLMs](https://mlsanity.com/paper/2505.22293/) (2025)
- [Translation Asymmetry in LLMs as a Data Augmentation Factor: A Case Study for 6 Romansh Language Varieties](https://mlsanity.com/paper/2603.25489/) (2026)
- [Exploring NLP Benchmarks in an Extremely Low-Resource Setting](https://mlsanity.com/paper/2509.03962/) (2025)
- [A Factorial Study of Synthetic Data Generation for Low-Resource Machine Translation using Grammar Books](https://mlsanity.com/paper/2607.22376/) (2026)

## Abstract (arXiv)

This paper explores the impact of different back-translation approaches on machine translation for Ladin, specifically the Val Badia variant. Given the limited amount of parallel data available for this language (only 18k Ladin-Italian sentence pairs), we investigate the performance of a multilingual neural machine translation model fine-tuned for Ladin-Italian. In addition to the available authentic data, we synthesise further translations by using three different models: a fine-tuned neural model, a rule-based system developed specifically for this language pair, and a large language model. Our experiments show that all approaches achieve comparable translation quality in this low-resource scenario, yet round-trip translations highlight differences in model performance.