Calibrating LLM Explanations to Human Decisions with Reinforcement Learning
Explanations of LLM outputs are considered an important way to make model behavior legible, helping human users make good decisions on downstream tasks. However, default training approaches for improving explanations target model task performance or human preference rather than human decision quality. To bridge this gap, we introduce \textbf{HD-RL}, a reinforcement learning framework for optimizing LLM explanations for human decision making. Our approach calibrates a human decision simulator on observed human decisions, applying a proxy reward model to optimize the LLM to generate explanations that help people to make good decisions. We apply this framework to AI-generated text detection....
| Authors | Dong Shu, Ziyang Guo, Negar Kamali et al. |
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| Primary category | cs.HC |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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