# Goal-Driven Multi-Agent Simulation Framework for Evaluating Recommender Systems under Data Scarcity
## Repository Contents
| Directory | Description |
|-----------|-------------|
| `african_health_qa/` | Multilingual African Health QA solution for the Zindi competition |
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## African Health QA — Zindi Competition
**Competition**: Multilingual Health QA in Low-Resource African Languages
**Approach**: Self-Active Learning fine-tuning of open-source African-language encoders, directly inspired by AfroLM (Dossou et al., arXiv:2211.03263).
See `african_health_qa/README.md` for full documentation.
### Key Design Choices
- **Model**: `Davlan/afro-xlmr-base` — XLM-RoBERTa fine-tuned with Multilingual Adaptive Fine-Tuning (MAFT) on 17+ African languages. Fully open-source alternative to AfroLM.
- **Task format**: Multiple-choice QA — encodes all 4 `(question, choice)` pairs, scores each with a linear head, picks argmax.
- **Data efficiency**: Self-Active Learning loop (3 rounds) selects the most uncertain samples from a held-out pool at each round and augments them using iterative MLM masking (AfroLM Algorithm 1 adapted for fine-tuning).
- **Languages supported**: Hausa · Swahili · Yorùbá · isiZulu · isiXhosa (+ extensible to all 23 AfroLM languages via config).