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When Audit Quality Fails to Predict Downstream Utility: A Counterfactual Study of Synthetic-Data Selectors for Low-Resource African NLP
Quality-aware synthetic-data selection rests on a proxy: examples that an LLM judge rates as good should also help a downstream model learn. In a controlled replay in low-resource African-language classification, we show that this proxy breaks. Across four languages (Amharic, Hausa, Swahili, Yoruba)
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