NH-CROP: Robust Pricing for Governed Language Data Assets under Cost Uncertainty
A new pricing framework for language data assets, NH-CROP, addresses cost uncertainty by using a clipped robust approach with a no-harm information-acquisition gate. The method decides whether to pay for refined cost signals before pricing, comparing direct pricing, risk-aware pricing, and verify-then-price strategies. Benchmarked on synthetic, real-proxy, and downstream-utility-grounded tasks, NH-CROP variants improve or match baselines. Causal ablations show paid verification is not always beneficial.
Key facts
- NH-CROP is a clipped robust pricing framework with a no-harm information-acquisition gate.
- The framework operates under cost uncertainty for governed language data assets.
- At each round, the platform observes an NLP task, a candidate asset, and a coarse cost estimate.
- The method compares direct pricing, risk-aware pricing, and verify-then-price.
- Information is acquired only when its estimated decision value exceeds the best no-verification alternative.
- Benchmarks include synthetic, real-proxy, and downstream-utility-grounded tasks.
- Clipped NH-CROP variants improve or remain competitive with price-only and risk-aware baselines.
- Causal ablations show that paid verification is not always beneficial.
Entities
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