arXiv:2603.13683v1 Announce Type: cross
Abstract: Although debiased LLMs perform well on known bias patterns, they often fail to generalize to unfamiliar bias prompts, producing toxic outputs. We first validate that such high-bias prompts constitute a emphdistribution shift via OOD detection, and show static models degrade under this shift. To adapt on-the-fly, we propose textbfCAP-TTA, a test-time adaptation framework that performs context-aware LoRA updates only when the bias-risk emphtrigger exceeds a threshold, using a precomputed diagonal emphpreconditioner for fast and stable updates. Across toxic-prompt settings and benchmarks, CAP-TTA reduces bias (confirmed by human evaluation) while achieving much lower update latency than AdamW/SGD; it also mitigates catastrophic forgetting by significantly improving narrative fluency over SOTA debiasing baseline while maintaining comparable debiasing effectiveness.
Telemedicine Adoption for Managing Chronic and Rare Diseases in Indonesia During and Beyond the COVID-19 Era: Qualitative Study
Background: Telemedicine has emerged as a valuable tool for improving health care delivery, especially in low-resource and geographically isolated regions. In Indonesia, the COVID-19 pandemic