AfriqueLLM: How Data Mixing and Model Architecture Impact Continued Pre-training for African Languages
arXiv:2601.06395v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly multilingual, yet open models continue to underperform relative to proprietary systems, with the gap most pronounced for African languages. Continued pre-training (CPT) offers a practical route to language adaptation, but improvements on demanding capabilities such as mathematical reasoning often remain limited. This limitation is driven in part by the uneven domain coverage and missing task-relevant knowledge that characterize many low-resource language corpora. We present \texttt{AfriqueLLM}, a
The increasing multilingual capabilities of LLMs and the recognition of their underperformance for African languages drive the timing for research into continued pre-training methods.
This research provides a practical pathway for AI adaptation for African languages, addressing the 'last mile' problem of AI development and fostering regional digital self-determination.
The explicit focus on data mixing and model architecture for continued pre-training specifically for African languages represents a targeted approach to reduce dependency on proprietary models.
- · African AI developers
- · African language communities
- · Organizations operating in African markets
- · Proprietary LLM providers with limited African language support
- · Generic multilingual LLM approaches
Improved performance of open foundation models for African languages becomes more achievable through targeted continued pre-training.
Increased adoption and utility of AI applications across various sectors within African nations, powered by locally relevant models.
Reduced reliance on external AI infrastructure and increased capacity for local innovation, fostering digital sovereignty on the continent.
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Read at arXiv cs.CL