Sunflower: A New Approach To Expanding Coverage of African Languages in Large Language Models
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Abstract (arXiv)
There are more than 2000 living languages in Africa, most of which have been bypassed by advances in language technology. Current leading LLMs exhibit strong performance on a number of the most common languages (e.g. Swahili or Yoruba), but prioritise support for the languages with the most speakers first, resulting in piecemeal ability across disparate languages. We contend that a regionally focussed approach is more efficient, and present a case study for Uganda, a country with high linguistic diversity. We describe the development of Sunflower 14B and 32B, a pair of models based on Qwen 3 with state of the art comprehension in the majority of all Ugandan languages. These models are open source and can be used to reduce language barriers in a number of important practical applications.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 37 of its 48 references that are in the map. First posted on arXiv 8 Oct 2025.
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References · 37
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2024The Llama 3 Herd of Modelscited by 14,715
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
- 2025Qwen3 Technical Reportcited by 8,820
- 2020Measuring Massive Multitask Language Understandingcited by 7,963
- 2023Qwen Technical Reportcited by 3,344
- 2020The Pile: An 800GB Dataset of Diverse Text for Language Modelingcited by 2,411
- 2022BLOOM: A 176B-Parameter Open-Access Multilingual Language Modelcited by 1,984
- 2020mT5: A massively multilingual pre-trained text-to-text transformercited by 1,818
- 2019Common Voice: A Massively-Multilingual Speech Corpuscited by 1,384
- 2022No Language Left Behind: Scaling Human-Centered Machine Translationcited by 1,312
- 2020COMET: A Neural Framework for MT Evaluationcited by 1,033
25 more
- 2024Gemma: Open Models Based on Gemini Research and Technologycited by 916
- 2020The State and Fate of Linguistic Diversity and Inclusion in the NLP Worldcited by 798
- 2015Improving Neural Machine Translation Models with Monolingual Datacited by 788
- 2020XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalizationcited by 742
- 2023The Flan Collection: Designing Data and Methods for Effective Instruction Tuningcited by 739
- 2020AdapterHub: A Framework for Adapting Transformerscited by 444
- 2021The GEM Benchmark: Natural Language Generation, its Evaluation and Metricscited by 239
- 2023MADLAD-400: A Multilingual And Document-Level Large Audited Datasetcited by 181
- 2020A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarioscited by 171
- 2020Participatory Research for Low-resourced Machine Translation: A Case Study in African Languagescited by 159
- 2023SeaLLMs -- Large Language Models for Southeast Asiacited by 104
- 2022A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translationcited by 98
- 2020Masakhane -- Machine Translation For Africacited by 49
- 2022Towards Afrocentric NLP for African Languages: Where We Are and Where We Can Gocited by 47
- 2023AfroBench: How Good are Large Language Models on African Languages?cited by 43
- 2024IrokoBench: A New Benchmark for African Languages in the Age of Large Language Modelscited by 43
- 2021Many-to-English Machine Translation Tools, Data, and Pretrained Modelscited by 31
- 2023AfriMTE and AfriCOMET: Enhancing COMET to Embrace Under-resourced African Languagescited by 24
- 2024Adapting Multilingual LLMs to Low-Resource Languages using Continued Pre-training and Synthetic Corpuscited by 24
- 2025SEA-LION: Southeast Asian Languages in One Networkcited by 22
- 2024Sailor: Open Language Models for South-East Asiacited by 19
- 2024InkubaLM: A small language model for low-resource African languagescited by 18
- 2024Challenges in Adapting Multilingual LLMs to Low-Resource Languages using LoRA PEFT Tuningcited by 14
- 2025Rethinking Multilingual Continual Pretraining: Data Mixing for Adapting LLMs Across Languages and Resourcescited by 10
- 2025Continually Adding New Languages to Multilingual Language Modelscited by 7
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