A competitive multi-group fine-tuning framework with point rewards correlated to Shapley values reduced simulated model distance by up to 55% and tracked user contributions reasonably in vector-space experiments.
Transparent Contribution Evaluation for Secure Federated Learning on Blockchain
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Crowd-SFT: Crowdsourcing for LLM Alignment
A competitive multi-group fine-tuning framework with point rewards correlated to Shapley values reduced simulated model distance by up to 55% and tracked user contributions reasonably in vector-space experiments.