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%.

Product families in large product catalogs suffer from inconsistencies such as duplicates and unit mismatches that degrade customer experience. Detecting these requires reasoning over multiple error types across lengthy product listings, where LLM classification quality degrades due to long-context limitations. We address this by decomposing detection into focused sub-tasks that reduce context and isolate error types, improving F1 from 52% to 87%. For scalable deployment, we introduce Positional Task Conditioning (PTC), which distills this capability into a single smaller model by reinforcing task identity at structural prompt boundaries. PTC outperforms rationale-based distillation across five models and two architecture families, achieving within 1.79% F1 of the frontier at upto 98% lower cost. Our system is deployed across multiple countries processing 10+ million product families.

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.

We introduce a novel, training free cascade for auto-prompting Large Language Models (LLMs) to assess product quality in e-commerce. Our system requires no training labels or model fine-tuning, instead automatically generating and refining prompts for evaluating attribute quality across tens of thousands of product category–attribute pairs. Starting from a seed of human-crafted prompts, the cascade progressively optimizes instructions to meet catalog-specific requirements. This approach bridges the gap between general language understanding and domain-specific knowledge at scale in complex industrial catalogs. Our extensive empirical evaluations shows the auto-prompt cascade improves precision and recall by 8–10% over traditional chain-of-thought prompting. Notably, it achieves these gains while reducing domain expert effort from 5.1 hours to 3 minutes per attribute - a 99% reduction. Additionally, the cascade generalizes effectively across five languages and multiple quality assessment tasks, consistently maintaining performance gains.

E-commerce stores increasingly use Large Language Models (LLMs) to enhance catalog data quality through automated regeneration. A critical challenge is accurately predicting missing structured attribute values across multilingual product catalogs, where LLM performance varies significantly by language. While existing approaches leverage general knowledge through prompt engineering and external retrieval, more effective and accurate signals for attribute prediction can exist within the catalog ecosystem itself-similar products often share consistent patterns and structural relationships, and may have the missing attributes filled. Therefore, this paper introduces PatternRAG, a novel retrieval-augmented system that strategically leverages existing product catalog entries to guide LLM predictions for missing attributes. Our approach introduces a multi-stage retrieval framework that progressively refines the search space based on product type, uses textual similarity, glance views and brand relationships to identify the most relevant attribute-filled examples for LLM prediction guidance. Experiments on test sets across three major e-commerce stores in different languages (US, DE, FR) demonstrate substantial improvements in catalog data quality, achieving up to 34% increase in recall and 0.8% in precision for attribute value prediction. At catalog entry level, it also achieves up to +43.32% increase in completeness and up to +2.83% in correctness.