Distribution-Aware Reward optimizes LLM regression by treating rollouts as empirical predictive distributions and rewarding marginal improvements in CRPS quality rather than point accuracy alone.
arXiv preprint arXiv:2411.14708 , year =
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
LLM framework converts facial action unit sequences to text, fuses with responses, and regresses to personality scores, reporting lower errors and higher correlations than baselines on AVI-6.
Leakage-controlled LLM factor ranking yields median Spearman IC of +0.154 that is largely matched by a kNN baseline on the same real-time macro inputs.
citing papers explorer
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Distribution-Aware Reward: Reinforcement Learning over Predictive Distributions for LLM Regression
Distribution-Aware Reward optimizes LLM regression by treating rollouts as empirical predictive distributions and rewarding marginal improvements in CRPS quality rather than point accuracy alone.
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LLM-based Multimodal Personality Recognition via Facial Action Unit-Text Semantic Fusion
LLM framework converts facial action unit sequences to text, fuses with responses, and regresses to personality scores, reporting lower errors and higher correlations than baselines on AVI-6.
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Leakage-Aware Benchmarking of LLM Forecasting: Real-Time Nowcasts as the Decision-Time Input for Macro Factor Ranking
Leakage-controlled LLM factor ranking yields median Spearman IC of +0.154 that is largely matched by a kNN baseline on the same real-time macro inputs.