A multi-dimensional behavioral scoring system using LLM judges evaluates agentic stock predictors and feeds scores into closed-loop RL to improve one-day MAPE by 11.5% on held-out data.
G-Eval: NLG evaluation using GPT-4 with better human alignment,
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Multi-Dimensional Behavioral Evaluation of Agentic Stock Prediction Systems Using Large Language Model Judges with Closed-Loop Reinforcement Learning Feedback
A multi-dimensional behavioral scoring system using LLM judges evaluates agentic stock predictors and feeds scores into closed-loop RL to improve one-day MAPE by 11.5% on held-out data.