In the field of medical decision-making, understanding the treatment intentions of clinicians is crucial for effective treatment strategies. However, these intentions are often implicit and challenging to quantify. In this paper, we propose a novel two-module model to infer and implement clinicians' treatment intentions through treatment records. We construct the InfCTI module, which infers intentions and quantifies them numerically, and the ImpCTI module, which generates treatment strategies based on inferred intentions. Our experiments demonstrate that the treatment strategies obtained by ImpCTI reflect clinicians' intentions and the intention values obtained by InfCTI are reasonable. This model has the potential to improve the quality of care provided to patients.

Large language models are increasingly being used for education and science communication by automatically generating explanations of scientific concepts. However, prior research has found that the analogies produced by LLMs lack human-like psycholinguistic properties important for readability. In this work, we propose cognitive conflict reinforcement learning (CCRL) to improve the psycholinguistic properties of analogies generated by LLMs. Specifically, we create cognitive conflict between the original LLM and a cloned LLM during reinforcement learning. This helps address the cognitive rigidity problem in LLMs. Experimental results demonstrate that our approach significantly outperforms existing RL algorithms and human performance in improving various readability metrics of generated analogies.