# LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models

arXiv: https://arxiv.org/abs/2411.13453 · HTML: https://mlsanity.com/paper/2411.13453/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 64 references that are in the map.
- First posted on arXiv 20 Nov 2024.

## Builds on (most cited first, 21 of 21)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [Data Selection for Language Models via Importance Resampling](https://mlsanity.com/paper/2302.03169/) (2023)
- [Transfer Learning for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/1604.02201/) (2016)
- [Metrics for Multi-Class Classification: an Overview](https://mlsanity.com/paper/2008.05756/) (2020)
- [Low-resource Languages: A Review of Past Work and Future Challenges](https://mlsanity.com/paper/2006.07264/) (2020)
- [LLMs Are Few-Shot In-Context Low-Resource Language Learners](https://mlsanity.com/paper/2403.16512/) (2024)
- [Fine-tuning Large Language Models for Domain-specific Machine Translation](https://mlsanity.com/paper/2402.15061/) (2024)
- [Language Model Prior for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/2004.14928/) (2020)
- [End-to-end Text-to-speech for Low-resource Languages by Cross-Lingual Transfer Learning](https://mlsanity.com/paper/1904.06508/) (2019)
- [Analyzing ASR pretraining for low-resource speech-to-text translation](https://mlsanity.com/paper/1910.10762/) (2019)
- [adaptMLLM: Fine-Tuning Multilingual Language Models on Low-Resource Languages with Integrated LLM Playgrounds](https://mlsanity.com/paper/2403.02370/) (2024)
- [Low-Resource Speech-to-Text Translation](https://mlsanity.com/paper/1803.09164/) (2018)
- [Model Transfer for Tagging Low-resource Languages using a Bilingual Dictionary](https://mlsanity.com/paper/1705.00424/) (2017)
- [Cross-Lingual Morphological Tagging for Low-Resource Languages](https://mlsanity.com/paper/1606.04279/) (2016)
- [Learning when to trust distant supervision: An application to low-resource POS tagging using cross-lingual projection](https://mlsanity.com/paper/1607.01133/) (2016)
- [A Grounded Unsupervised Universal Part-of-Speech Tagger for Low-Resource Languages](https://mlsanity.com/paper/1904.05426/) (2019)
- [Introducing Syllable Tokenization for Low-resource Languages: A Case Study with Swahili](https://mlsanity.com/paper/2406.15358/) (2024)
- [Building pre-train LLM Dataset for the INDIC Languages: a case study on Hindi](https://mlsanity.com/paper/2407.09855/) (2024)
- [Practical Comparable Data Collection for Low-Resource Languages via Images](https://mlsanity.com/paper/2004.11954/) (2020)
- [An Augmented Translation Technique for low Resource language pair: Sanskrit to Hindi translation](https://mlsanity.com/paper/2006.08332/) (2020)

## Abstract (arXiv)

Minority languages are vital to preserving cultural heritage, yet they face growing risks of extinction due to limited digital resources and the dominance of artificial intelligence models trained on high-resource languages. This white paper proposes a framework to generate linguistic tools for low-resource languages, focusing on data creation to support the development of language models that can aid in preservation efforts. Sardinian, an endangered language, serves as the case study to demonstrate the framework's effectiveness. By addressing the data scarcity that hinders intelligent applications for such languages, we contribute to promoting linguistic diversity and support ongoing efforts in language standardization and revitalization through modern technologies.