Withdrawal rights paired with centralized cost-based assignment prevent subsidy waste by collecting data only when the improvement threshold is sustainably reachable, turning infeasible cases into null outcomes.
Position: Will we run out of data? limits of llm scaling based on human-generated data
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
Incentivizing User Data Contributions for LLM Improvement under Withdrawal Rights
Withdrawal rights paired with centralized cost-based assignment prevent subsidy waste by collecting data only when the improvement threshold is sustainably reachable, turning infeasible cases into null outcomes.
- RISE: Reliable Improvement in Self-Evolving Vision-Language Models