@inproceedings{bibcite_16164,
  author = {Nur Kelesoglu and Joanna Doma{\'n}ska and {\L}ukasz Sobczak},
  title = {Risk-Aware Response Refinement for Safer LLM-Powered Socially Assistive Robots in Elderly Care},
  abstract = {<p>Safe and reliable human{\textendash}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. \&nbsp;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.</p>},
  year = {2026},
  journal = {IEEE International Conference on Robot and Human Interactive Communication (IEEE RO-MAN 2026)},
  address = {Fukuoka, Japan},
}