test-time-adaptation

This paper proposes CAP-TTA, a test-time adaptation framework that uses preconditioned LoRA updates triggered by bias-risk scores to mitigate toxicity and bias in large language models during narrative generation, achieving faster optimization and better fluency than standard baselines.

TTL introduces a test-time textual learning framework for OOD detection using pretrained vision-language models like CLIP, which dynamically learns OOD semantics from unlabeled test streams without external OOD labels. The method uses pseudo-labeled samples and an OOD knowledge purification strategy to improve detection robustness across diverse and evolving OOD distributions.