Risk-Aware Response Refinement for Safer LLM-Powered Socially Assistive Robots in Elderly Care

Safe and reliable human–robot interaction remains a challenge, especially for Large Language Model (LLM)-powered socially assistive robots in elderly care. This paper proposes a risk-aware response framework that explicitly models the safety implications of user queries.  We introduce a Query Risk Assessment Module (QRAM), which computes a Query Risk Score (QRS) using structured semantic indicators to classify user inputs into different risk levels. Based on this, we develop two strategies: risk-aware response refinement and risk-aware response generation. To assess safety, we introduce the Risk-Aware Response Safety Score (RRSS), a metric that captures both the presence of risk-bearing language and the absence of necessary safety-critical guidance. Experimental results across multiple LLMs demonstrate that incorporating query-level risk awareness reduces response-level risk, with both strategies outperforming raw responses.