TA-RAG: Tone-Aware Retrieval-Augmented Generation for Peer-Support Health Communication
Retrieval-augmented generation (RAG) successfully grounds large language model (LLM) outputs in trusted documents, but factual grounding alone is insufficient for sensitive peer-support health communication. In domains such as HIV peer support, responses must also be accessible, stigma-free, empathetic, and tailored to the recipient. This paper presents TA-RAG, a lightweight, prompt-based tone-aware RAG framework that embeds explicit tone control into a RAG pipeline without requiring model fine-tuning. We operationalise tone across four core components: stigma-free rewriting, readability adjustment, recipient adaptation, and empathy rephrasing. We evaluate TA-RAG through component-level t...
| Authors | Yong-Bin Kang, Anthony McCosker |
|---|---|
| Primary category | cs.CL |
| Published | 5 June 2026 |
| Last updated at source | 11 June 2026 |
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