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cs.CV2024
Med-PerSAM: One-Shot Visual Prompt Tuning for Personalized Segment Anything Model in Medical Domain
Hangyul Yoon, Doohyuk Jang, Jungeun Kim +1
Leveraging pre-trained models with tailored prompts for in-context learning has proven highly effective in NLP tasks. Building on this success, recent studies have applied a simila…
cs.CL2024
PromptKD: Distilling Student-Friendly Knowledge for Generative Language Models via Prompt Tuning
Gyeongman Kim, Doohyuk Jang, Eunho Yang
Recent advancements in large language models (LLMs) have raised concerns about inference costs, increasing the need for research into model compression. While knowledge distillatio…