Effective text generation and chat interfaces for low-resource languages (LRLs) remain a challenge for state-of-the-art large language models (LLMs) to support. This is mainly due to the difficulty of curating high-quality instruction datasets for LRLs, a limitation prevalent in the languages spoken across the African continent and other regions. Current approaches, such as automated translation and synthetic data generation, frequently yield outputs that lack fluency or even orthographic consistency. In this paper, we introduce InstructLR, a novel framework designed to generate high-quality instruction datasets for LRLs. Our approach integrates LLM-driven text generation with a dual-layer quality filtering mechanism: an automated filtering layer based on retrieval-augmented-generation (RAG)-based n-shot prompting, and a human-in-the-loop validation layer. Drawing inspiration from benchmarks such as MMLU in task definition, InstructLR has facilitated the creation of three multi-domain instruction benchmarks: **ZarmaInstruct-50k**, **BambaraInstruct-50k**, and **FulfuldeInstruct-50k**.

This paper introduces the first standardized benchmark for evaluating Automatic Speech Recognition (ASR) in the Bambara language, utilizing one hour of professionally recorded Malian constitutional text. Designed as a controlled reference set under near-optimal acoustic and linguistic conditions, the benchmark was used to evaluate 37 models, ranging from Bambara-trained systems to large-scale commercial models. Our findings reveal that current ASR performance remains significantly below deployment standards; the top-performing system in terms of Word Error Rate (WER) achieved 46.76% and the best Character Error Rate (CER) of 13.00% was set by another model, while several prominent multilingual models exceeded 100% WER due to severe hallucinations. These results suggest that multilingual pre-training and model scaling alone are insufficient for underrepresented languages. Furthermore, because this dataset represents a best-case scenario of the most simplified and formal form of spoken Bambara, these figures likely establish an upper bound for performance in practical, real-world settings. We provide the benchmark and an accompanying public leaderboard to facilitate transparent evaluation and future research in Bambara speech technology.

Grammatical error correction (GEC) aims to improve text quality and readability. Previous work on the task focused primarily on high-resource languages, while low-resource languages lack robust tools. To address this shortcoming, we present a study on GEC for Zarma, a language spoken by over five million people in West Africa. We compare three approaches: rule-based methods, machine translation (MT) models, and large language models (LLMs). We evaluated GEC models using a dataset of more than 250,000 examples, including synthetic and human-annotated data. Our results showed that the MT-based approach using M2M100 outperforms others, with a detection rate of 95.82% and a suggestion accuracy of 78.90% in automatic evaluations (AE) and an average score of 3.0 out of 5.0 in manual evaluation (ME) from native speakers for grammar and logical corrections. The rule-based method was effective for spelling errors but failed on complex context-level errors. LLMs—Gemma 2b and MT5-small—showed moderate performance. Our work supports use of MT models to enhance GEC in low-resource settings, and we validated these results with Bambara, another West African language.

Illiteracy is a predictor of many negative social and personal outcomes. Illiteracy rates are particularly high in countries with underresourced languages, where few books exist that are suitable for children to learn to read from. We present GAIfE (Generative AI for Education), a toolchain and workflow developed through empirical methods, that demonstrates how existing tools can be adapted to address low literacy for an underresourced language. We used GAIfE (a play on the Bambara word for “book”) to construct materials for developing children’s reading competence in Bambara, the vehicular language of Mali. Our approach to the generation and post-generation editing of content skewed by the Global-North-centric bias of available LLMs, enabled us to rapidly multiply the content in Bambara available online by 10 times while maintaining high standards of attractiveness of the material to maintain high engagement, accurate representation of the Malian culture and physical and social environment and language quality. Using our materials, pilot reading programs achieved a 67% reduction in the number of children unable to read Bambara. Our approach demonstrated the power of bias-aware application of generative AI to the problem domain as well as the potential impact the application of this technology could have on reducing illiteracy and improving learning outcomes through native language education.

We open-source SMOL (Set of Maximal Over-all Leverage), a suite of training data to un-lock machine translation for low-resource languages (LRLs). SMOL has been translated into123 under-resourced languages (125 language pairs), including many for which there exist no previous public resources, for a total of 6.1M translated tokens. SMOL comprises two sub-datasets, each carefully chosen for maximum impact given its size: SMOLSENT, a set of sentences chosen for broad unique token coverage, and SMOLDOC, a document-level source focusing on a broad topic coverage. They join the already released GATITOS for a trifecta of paragraph, sentence, and token-level content. We demonstrate that using SMOL to prompt or fine-tune Large Language Models yields robust chrF improvements. In addition to translation, we provide factuality ratings and rationales for all documents in SMOLDOC, yielding the first factuality datasets for most of these languages.