LiLaVe, an XGBoost verifier trained on hidden states of a base LLM, predicts answer correctness with AUC comparable to large LLM-based verifiers, and enables conditional majority voting and self-correction that improve accuracy under fixed generation budgets.
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Lightweight Latent Verifiers for Efficient Meta-Generation Strategies
LiLaVe, an XGBoost verifier trained on hidden states of a base LLM, predicts answer correctness with AUC comparable to large LLM-based verifiers, and enables conditional majority voting and self-correction that improve accuracy under fixed generation budgets.