CalibraRL: Confidence-Calibrated Reinforcement Learning for Mitigating Hallucinations in RAG
Large language models (LLMs) often produce plausible but incorrect answers, motivating reinforcement learning methods that reward correct answers, penalize incorrect answers, and allow abstention. However, fixed outcome-based rewards do not distinguish incorrect responses with different confidence levels or account for...
Qianchi Zhang, Hainan Zhang, Liang Pang et al.
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