Optimal additive baseline IPS asymptotically dominates SNIPS in off-policy evaluation mean squared error.
and Chen, Minmin , title =
2 Pith papers cite this work. Polarity classification is still indexing.
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IAP uses RL to train LLMs to explicitly infer and apply implicit user intent in single-turn personalized QA, achieving ~7.5% average macro-score gains over baselines on LaMP-QA.
citing papers explorer
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Additive Control Variates Dominate Self-Normalisation in Off-Policy Evaluation
Optimal additive baseline IPS asymptotically dominates SNIPS in off-policy evaluation mean squared error.
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Training LLMs with Reinforcement Learning for Intent-Aware Personalized Question Answering
IAP uses RL to train LLMs to explicitly infer and apply implicit user intent in single-turn personalized QA, achieving ~7.5% average macro-score gains over baselines on LaMP-QA.