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This paper presents TA-RAG, a prompt-based framework that adds explicit tone control to retrieval-augmented generation for sensitive health communication, such as HIV peer support, without requiring fine-tuning. It evaluates components like stigma-free rewriting, readability, recipient adaptation, and empathy rephrasing.
New research shows that training AI chatbots to be warmer and more empathetic significantly reduces their factual accuracy, leading to higher error rates in medical advice and increased agreement with user misconceptions. The findings challenge the common assumption that conversational style can be adjusted without compromising factual correctness.
This paper introduces the Adversarial Empathy Benchmark (AEB) and Emotional Consistency Score (ECS) to test the robustness of RLVER-trained models against adversarial user behaviors. Results show that while RLVER improves emotional responsiveness, it does not significantly enhance the model's ability to track user emotional states under adversarial conditions.
WiseMind is a knowledge-guided multi-agent framework that uses LLMs for psychiatric diagnosis by combining a "Reasonable Mind" agent for evidence-based logic with an "Emotional Mind" agent for empathetic communication, achieving 85.6% diagnostic accuracy on simulated and real patient interactions. The framework leverages DSM-5 structured knowledge graphs to reduce hallucinations and outperforms single-agent baselines by 15-54 percentage points while maintaining clinical soundness and psychological support.