1 citations · 1 across the 5 of their papers we have counts for
5 papers
SyRuP: Enhancing System-Prompt Following via Reward-Guided Prediction in LLM Decoding
Seoyeon Kim, Minjae Kang, Jaehyung Kim
Large Language Models (LLMs) are increasingly controlled through system prompts that specify roles, formats, and safety requirements. However, models follow these prompts only impl…
Enhancing Instruction Following of LLMs via Activation Steering with Dynamic Rejection
Minjae Kang, Jaehyung Kim
Large Language Models (LLMs), despite advances in instruction tuning, often fail to follow complex user instructions. Activation steering techniques aim to mitigate this by manipul…
Riemannian Optimization for LoRA on the Stiefel Manifold
Juneyoung Park, Minjae Kang, Seongbae Lee +3
While powerful, large language models (LLMs) present significant fine-tuning challenges due to their size. Parameter-efficient fine-tuning (PEFT) methods like LoRA provide solution…
Personalized LLM Decoding via Contrasting Personal Preference
Hyungjune Bu, Chanjoo Jung, Minjae Kang +1
As large language models (LLMs) are progressively deployed in various real-world applications, personalization of LLMs has become increasingly important. While various approaches t…
Fast Adaptation with Kernel and Gradient based Meta Leaning
JuneYoung Park, MinJae Kang
Model Agnostic Meta Learning or MAML has become the standard for few-shot learning as a meta-learning problem. MAML is simple and can be applied to any model, as its name suggests.…