8 papers
ACE: Adapter Consolidation across Experts for Parameter-Efficient Fine-Tuning of MoE LLMs
Ahin Lee, Sehyun Yun, Joonha Park +1
Parameter-efficient fine-tuning (PEFT) of mixture-of-experts (MoE) models commonly attaches a separate low-rank adapter to each expert. This expert-wise design fragments adaptation…
EPnG: Adaptive Expert Prune-and-Grow for Parameter-Efficient MoE Fine-tuning
Ahin Lee, Sehyun Yun, Taesik Gong
Mixture-of-Experts (MoE) models scale efficiently but remain costly to adapt due to redundant experts and uniform parameter allocation. Existing parameter-efficient fine-tuning (PE…
ExPerT: Personalizing LLM Responses to Users' Domain Expertise via Query-Wise Semantic and Keystroke Behavioral Cues
Yeji Park, Jiwon Tark, Taesik Gong
Large language models (LLMs) are increasingly used by end users, yet existing personalization methods relying on static profiles or text-only signals fail to capture query-specific…
From Volume to Value: Preference-Aligned Memory Construction for On-Device RAG
Changmin Lee, Jaemin Kim, Taesik Gong
With the rapid emergence of personal AI agents based on Large Language Models (LLMs), implementing them on-device has become essential for privacy and responsiveness. To handle the…
Premover: Fast Vision-Language-Action Control by Acting Before Instructions Are Complete
Joonha Park, Jiseung Jeong, Taesik Gong
Vision-Language-Action (VLA) policies are typically evaluated as if the user had finished typing or speaking before the robot begins acting. In real deployment, however, users take…
DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators
Taesik Gong, Fahim Kawsar, Chulhong Min
Tiny machine learning (TinyML) aims to run ML models on small devices and is increasingly favored for its enhanced privacy, reduced latency, and low cost. Recently, the advent of t…