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arXiv · 2609.18960

When Audit Quality Fails to Predict Downstream Utility: A Counterfactual Study of Synthetic-Data Selectors for Low-Resource African NLP

Son Ha Xuan·Phat T. Tran-Truong·Xuan-Bach Le
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Abstract

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), two classification tasks (MasakhaNEWS, AfriSenti), and five matched-budget selectors, audit rankings and downstream rankings diverge. Within each cell, the Spearman between judged label correctness and Macro-F1 across selectors has mean $ρ{=}0.04$ (median $0.00$), showing that the mismatch is not an aggregation artifact. \method{}-V2, our counterfactual audit framework, produces the cleanest selected pool on three audit channels at once: highest judged label correctness ($0.904$ vs.\ $0.767$ for naive, a $17.9\%$ relative gain), lowest shortcut score, and a hard-reject rate of $0.162$ vs.\ $0.486$ for naive. AlpaGasus nevertheless leads downstream Macro-F1 ($0.202$ vs.\ $0.163$ for \method{}-V2), and the inversion persists on the five non-degenerate cells. The lesson is methodological: in this controlled setting, audit quality is a property of the selected pool, not a guarantee of downstream utility. Synthetic-data evaluation should therefore report audit and downstream metrics on the same retained sets. We release the audit tables, per-selector retained pools, and a claim ledger that links every reported number to its source row.

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Son Ha Xuan, Phat T. Tran-Truong, Xuan-Bach Le. 2026-07-15. When Audit Quality Fails to Predict Downstream Utility: A Counterfactual Study of Synthetic-Data Selectors for Low-Resource African NLP. https://arxiv.org/abs/2609.18960

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