The arbitration step

• Receives predictions + scores from LCS, TTC, RAG
• The LLM sees: the product label, shop context, the three candidates, and a filtered COICOP nomenclature (codes + labels)
• Selects the most appropriate code and returns a confidence score (1–5)

Why LLM rather than majority vote?

A vote counts voices but the LLM weights quality signals (confidence, distance) and uses the full purchase context to resolve what no method could distinguish alone.

Two cost-saving shortcuts

1. Consensus short-circuit : if all three methods agree and TTC confidence ≥ 0.90 → no LLM call, prediction accepted directly
2. Classification filtering : only the relevant COICOP sections are injected → 4–10× fewer tokens

Current limits

• Strong dependency on the quantity and quality of annotated data:
  • Collecting 3M annotated labels for TTC training : high effort, hard to scale
  • Accuracy underestimated : ~10% error rate in manual annotations inflates apparent errors
  • Sparse coverage for some COICOP divisions (level 1)
• Poor reliability of confidence indicators to target manual review effectively
• Deployment challenges (GPU requirements)

Improvements

• Add additional inputs from 2017 HBS labels
• Improve the LCS suggester with richer (product, code) pair context
• Improve the TTC model with a receipt-specific tokenizer
• Log all RAG traces in Langfuse, not just prompt versioning

A hybrid, production-ready pipeline

• Rule-based pre-filter handles high-confidence, easy cases at zero ML cost
• Three different coding methods : LCS, TTC, RAG
• LLM-as-Judge arbitrates intelligently between them
• Fully open-source MLOps on SSPCloud

The key message

• Product and activity coding is a recurring bottleneck across official statistics.
• Our method offers a viable path forward for any survey requiring classification.
• TTC can be retrained for any classification task or language, the main requirement is an annotated training dataset.