Balancing Knowledge Delivery and Emotional Comfort in Healthcare Conversational Systems
This addresses the need for more reassuring patient experiences in medical consultations by improving conversational AI's ability to provide both emotional support and factual information, though it represents an incremental advancement in dialogue system refinement.
The paper tackles the problem of balancing medical knowledge delivery with emotional comfort in healthcare conversational systems by fine-tuning large language models on a rewritten dataset containing patient queries with negative emotions. Experimental results show the methodology significantly enhances emotional response generation while maintaining accurate knowledge-based answering capabilities.
With the advancement of large language models, many dialogue systems are now capable of providing reasonable and informative responses to patients' medical conditions. However, when patients consult their doctor, they may experience negative emotions due to the severity and urgency of their situation. If the model can provide appropriate comfort and empathy based on the patient's negative emotions while answering medical questions, it will likely offer a more reassuring experience during the medical consultation process. To address this issue, our paper explores the balance between knowledge sharing and emotional support in the healthcare dialogue process. We utilize a large language model to rewrite a real-world interactive medical dialogue dataset, generating patient queries with negative emotions and corresponding medical responses aimed at soothing the patient's emotions while addressing their concerns. The modified data serves to refine the latest large language models with various fine-tuning methods, enabling them to accurately provide sentences with both emotional reassurance and constructive suggestions in response to patients' questions. Compared to the original LLM model, our experimental results demonstrate that our methodology significantly enhances the model's ability to generate emotional responses while maintaining its original capability to provide accurate knowledge-based answers.