# adaptMLLM: Fine-Tuning Multilingual Language Models on Low-Resource Languages with Integrated LLM Playgrounds arXiv: https://arxiv.org/abs/2403.02370 ยท HTML: https://mlsanity.com/paper/2403.02370/ ## Key facts - Cited by 14 papers in the map (data through 14 Oct 2026). - Builds on 19 of its 48 references that are in the map. - First posted on arXiv 4 Mar 2024. ## Builds on (most cited first, 19 of 19) - [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) - [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019) - [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018) - [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018) - [GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding](https://mlsanity.com/paper/2006.16668/) (2020) - [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022) - [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019) - [LaMDA: Language Models for Dialog Applications](https://mlsanity.com/paper/2201.08239/) (2022) - [Quantifying the Carbon Emissions of Machine Learning](https://mlsanity.com/paper/1910.09700/) (2019) - [Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation](https://mlsanity.com/paper/2104.14478/) (2021) - [Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning](https://mlsanity.com/paper/2002.05651/) (2020) - [Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild](https://mlsanity.com/paper/1906.02569/) (2019) - [Language Models are Few-shot Multilingual Learners](https://mlsanity.com/paper/2109.07684/) (2021) - [A Set of Recommendations for Assessing Human-Machine Parity in Language Translation](https://mlsanity.com/paper/2004.01694/) (2020) - [Exploiting Out-of-Domain Parallel Data through Multilingual Transfer Learning for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/1907.03060/) (2019) - [Findings of the LoResMT 2021 Shared Task on COVID and Sign Language for Low-resource Languages](https://mlsanity.com/paper/2108.06598/) (2021) - [Machine Translation in the Covid domain: an English-Irish case study for LoResMT 2021](https://mlsanity.com/paper/2403.01196/) (2024) - [Quantitative Fine-Grained Human Evaluation of Machine Translation Systems: a Case Study on English to Croatian](https://mlsanity.com/paper/1802.01451/) (2018) ## Built on by (most cited first, 14 of 14) - [Challenges in Adapting Multilingual LLMs to Low-Resource Languages using LoRA PEFT Tuning](https://mlsanity.com/paper/2411.18571/) (2024) - [Evaluating and Mitigating Linguistic Discrimination in Large Language Models](https://mlsanity.com/paper/2404.18534/) (2024) - [LLM-Assisted Rule Based Machine Translation for Low/No-Resource Languages](https://mlsanity.com/paper/2405.08997/) (2024) - [Decoding the Diversity: A Review of the Indic AI Research Landscape](https://mlsanity.com/paper/2406.09559/) (2024) - [Transferable Modeling Strategies for Low-Resource LLM Tasks: A Prompt and Alignment-Based Approach](https://mlsanity.com/paper/2507.00601/) (2025) - [Missing Melodies: AI Music Generation and its "Nearly" Complete Omission of the Global South](https://mlsanity.com/paper/2412.04100/) (2024) - [Synthetic Fluency: Hallucinations, Confabulations, and the Creation of Irish Words in LLM-Generated Translations](https://mlsanity.com/paper/2504.07680/) (2025) - [Towards a More Inclusive AI: Progress and Perspectives in Large Language Model Training for the S'ami Language](https://mlsanity.com/paper/2405.05777/) (2024) - [Leveraging LLMs for MT in Crisis Scenarios: a blueprint for low-resource languages](https://mlsanity.com/paper/2410.23890/) (2024) - [Exploring Continual Fine-Tuning for Enhancing Language Ability in Large Language Model](https://mlsanity.com/paper/2410.16006/) (2024) - [LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models](https://mlsanity.com/paper/2411.13453/) (2024) - [Culturally-Grounded Governance for Multilingual Language Models: Rights, Data Boundaries, and Accountable AI Design](https://mlsanity.com/paper/2602.00497/) (2026) - [A technical curriculum on language-oriented artificial intelligence in translation and specialised communication](https://mlsanity.com/paper/2602.12251/) (2026) - [Evaluating Fine-Tuned LLM Model For Medical Transcription With Small Low-Resource Languages Validated Dataset](https://mlsanity.com/paper/2603.24772/) (2026) ## Abstract (arXiv) The advent of Multilingual Language Models (MLLMs) and Large Language Models has spawned innovation in many areas of natural language processing. Despite the exciting potential of this technology, its impact on developing high-quality Machine Translation (MT) outputs for low-resource languages remains relatively under-explored. Furthermore, an open-source application, dedicated to both fine-tuning MLLMs and managing the complete MT workflow for low-resources languages, remains unavailable. We aim to address these imbalances through the development of adaptMLLM, which streamlines all processes involved in the fine-tuning of MLLMs for MT. This open-source application is tailored for developers, translators, and users who are engaged in MT. An intuitive interface allows for easy customisation of hyperparameters, and the application offers a range of metrics for model evaluation and the capability to deploy models as a translation service directly within the application. As a multilingual tool, we used adaptMLLM to fine-tune models for two low-resource language pairs: English to Irish (ENleftrightarrowGA) and English to Marathi (ENleftrightarrowMR). Compared with baselines from the LoResMT2021 Shared Task, the adaptMLLM system demonstrated significant improvements. In the ENrightarrowGA direction, an improvement of 5.2 BLEU points was observed and an increase of 40.5 BLEU points was recorded in the GArightarrowEN direction. Significant improvements in the translation performance of the ENleftrightarrowMR pair were also observed notably in the MRrightarrowEN direction with an increase of 21.3 BLEU points. Finally, a fine-grained human evaluation of the MLLM output on the ENrightarrowGA pair was conducted using the Multidimensional Quality Metrics and Scalar Quality Metrics error taxonomies. The application and models are freely available.