4 papers
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…
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 --…
sudo rm -rf agentic_security
Sejin Lee, Jian Kim, Haon Park +3
Large Language Models (LLMs) are increasingly deployed as computer-use agents, autonomously performing tasks within real desktop or web environments. While this evolution greatly e…
Beyond Ontology in Dialogue State Tracking for Goal-Oriented Chatbot
Sejin Lee, Dongha Kim, Min Song
Goal-oriented chatbots are essential for automating user tasks, such as booking flights or making restaurant reservations. A key component of these systems is Dialogue State Tracki…