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cs.CL2026
Quantifying Aleatoric Uncertainty of In-Context Learning for Robust Measure of LLM Prediction Confidence
Jinseok Chung, Minkyoung Song, Hyunji Jung +1
In-Context Learning (ICL) allows LLMs to adapt to new tasks from a few demonstrations, but its reliability remains a concern: predictions are highly sensitive to both prompt design…
cs.CV2026
Let Triggers Control: Frequency-Aware Dropout for Effective Token Control
Junyoung Koh, Hoyeon Moon, Dongha Kim +3
Text-to-image models such as Stable Diffusion have achieved unprecedented levels of high-fidelity visual synthesis. As these models advance, personalization of generative models --…