SynthAVE: Scalable Synthetic Labeling for E-Commerce with LLM-Arena Validation

arXiv:2607.07469v1 Announce Type: new Abstract: 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 human-validated benchmark for attribute value extract

Source: arXiv cs.CL — read the full report at the original publisher.

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