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Source note2 min read

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

Source

arXiv

arxiv.org/abs/2607.07469v1

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Plain-Language Brief

Researchers have developed a method called SynthAVE to generate large amounts of labeled data for e-commerce attribute extraction using synthetic labels validated by multiple large language models (LLMs). The benchmark includes 12,726 products in four languages, and the researchers show that the ensemble of diverse LLMs agrees with human experts at a high level, making it cost-effective and scalable.

What the Document Says

  • The research presents SynthAVE, a large-scale benchmark for attribute value extraction in e-commerce
  • The benchmark includes 12,726 products across 229 product types, 792 attributes, and 4 languages: Spanish, French, Italian, German
  • SynthAVE uses synthetic labels generated by large language models (LLMs)
  • To ensure the quality of synthetic labels at scale, a multi-LLM arena framework is introduced
  • The framework has 21 judge configurations (7 model families $ imes$ 3 prompts), and final labels are determined via majority voting
  • The ensemble of diverse LLMs agrees with human experts at Cohen's $κ= 0.92$ (95.2% agreement)
  • Individual judges show substantial inter-model agreement (Fleiss' $κ= 0.76$)
  • The method demonstrates that diverse models with varying individual judgments can aggregate into highly reliable predictions
  • This makes it cost-effective and scalable while maintaining quality parity with human review

Key Concepts Mentioned

  • e-commerce attribute extraction
  • large language models (LLMs)
  • synthetic labeling
  • human validation
  • multi-LLM arena

Claims Made by the Document

  • SynthAVE is a large-scale benchmark for attribute value extraction in e-commerce
  • The ensemble of diverse LLMs agrees with human experts at Cohen's $κ= 0.92$
  • Individual judges show substantial inter-model agreement (Fleiss' $κ= 0.76$)

Evidence Used

  • 12,726 products across 229 product types, 792 attributes, and 4 languages are included in the benchmark
  • The multi-LLM arena framework is introduced for quality control
  • Cohen's $κ= 0.92$ and Fleiss' $κ= 0.76$ values are provided to demonstrate agreement with human experts and inter-model agreement, respectively

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

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Limitations Noted by the Agent

Agent interpretation — not from the source.

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Open Questions

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Notes for Future Q&A

May include interpretation. Labeled where it does.

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Raw Abstract / Excerpt

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 extraction 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 samples are independently evaluated by 21 judge configurations (7 model families $\times$ 3 prompts), with final labels determined via majority voting. The majority vote ensemble agrees with human experts at Cohen's $κ= 0.92$ (95.2% agreement), while individual judges show substantial inter-model agreement (Fleiss' $κ= 0.76$). This demonstrates that diverse models with varying individual judgments aggregate into highly reliable predictions, enabling cost-effective validation at scale while maintaining quality parity with human review.