Fine-tuning large language models (LLMs) for e-commerce attribute extraction requires labeled data representative across thousands of product types, attributes, and multiple languages. This combinatorial scale translates to millions of annotations, rendering human labeling prohibitively costly. While recent work has demonstrated synthetic label generation using LLMs, deploying such approaches at industrial scale requires integrated quality control mechanisms. We present SynthAVE, a large-scale benchmark for attribute-value verification spanning 12,726 products across 229 product types, 792 attributes, and 4 languages (Spanish, French, Italian, German). To validate synthetic labels at scale, we introduce a multi-LLM arena framework where each sample is evaluated by 21 judge configurations (7 model families × 3 prompts), with final labels determined via majority voting; disagreements with the synthetic label are expert-adjudicated and agreement cases are audited on a stratified sample. The majority vote ensemble agrees with expert labels at Cohen’s 𝜅 = 0.92 (95.0% agreement), while individual judges agree with one another only moderately (Fleiss’ 𝜅 = 0.76)—by design, since we select for diversity. This demonstrates that diverse models with varying individual judgments aggregate into highly reliable predictions, enabling cost-effective validation at scale while concentrating expert effort on the cases where it changes the label. We estimate the resulting label quality at 97.9%.

Fine-tuned small language models (SLMs) are attractive defaults for large-scale structured at- tribute generation—producing values such as materials, colors, or dimensions from product descriptions—as they are inexpensive to serve. However, a fine-tuned SLM is not reliable ev- erywhere as its correctness varies sharply, so deploying it still requires deciding, per output, whether to keep the SLM’s value or escalate it to a more capable fallback. The standard pol- icy applies a single confidence threshold, but one threshold cannot separate scores on reli- able tasks from those on unreliable ones, so it reduces coverage at no gain in quality. We demonstrate that in a fixed deployment scope, where the target tasks recur and validation data is available, a lightweight scope-aware router using only SLM-side features (token confi- dence plus task-scope identity) selects risky predictions far better. Our experiments show that at a fixed precision target it accepts about 11 points more values than a global confidence threshold (family-level calibration helps only marginally). Ablation experiments confirm the gain comes from task-scope reliability priors rather than a better confidence transformation. The router is an 8-feature gradient-boosted tree that adds negligible inference cost, making a strong fine-tuned SLM the low-cost tier of a cascade that optimizes the quality–cost tradeoff in real production settings.