Quality-aware synthetic-data selection assumes that examples rated highly by an LLM judge will help downstream learning. We test this assumption in a controlled replay across two low-resource African-language classification tasks, four languages, and seven selectors. Six selectors retain 64 examples per task-language cell. CoSDA-V2 yields the highest judged label correctness (0.904 versus 0.767 for naive) and the lowest measured shortcut score, but AlpaGasus leads CoSDA-V2 on downstream Macro-F1 in both the main replay (0.202 versus 0.163) and the three-seed refit (0.193 versus 0.157).

Adding the audit’s strict gate to AlpaGasus reduces Macro-F1 by 0.029, driven by one cell where only 31 of 64 candidates survive. Because the gate changes pool composition and training-set size, this comparison measures their joint effect. LESS also falls below naive when unconstrained top-k collapses label balance, but recovers with quotas. At this budget, retention constraints account for more of the observed differences than a consistent ranking advantage. In this replay, audit scores do not reliably predict downstream utility. We therefore recommend reporting both metrics for the same retained sets.

Small Language Models (SLMs) offer computational efficiency and accessibility, yet a systematic evaluation of their performance and environmental impact remains lacking. We introduce SLM-Bench, the first benchmark specifically designed to assess SLMs across multiple dimensions, including accuracy, computational efficiency, and sustainability metrics. SLM-Bench evaluates 15 SLMs on 9 NLP tasks using 23 datasets spanning 14 domains. The evaluation is conducted on 4 hardware configurations, providing a rigorous comparison of their effectiveness. Unlike prior benchmarks, SLM-Bench quantifies 11 metrics across correctness, computation, and consumption, enabling a holistic assessment of efficiency trade-offs. Our evaluation considers controlled hardware conditions, ensuring fair comparisons across models. We develop an open-source benchmarking pipeline with standardized evaluation protocols to facilitate reproducibility and further research. Our findings highlight the diverse trade-offs among SLMs, where some models excel in accuracy while others achieve superior energy efficiency. SLM-Bench sets a new standard for SLM evaluation, bridging the gap between resource efficiency and real-world applicability.