4 papers
Fine-Tuning Language Models to Know What They Know
Sangjun Park, Elliot Meyerson, Xin Qiu +1
Evaluating true metacognition in Large Language Models (LLMs) is difficult due to biases and heuristics. This paper presents a framework to measure and enhance LLM metacognition wh…
Smart Contract-Enabled Procurement under Bounded Demand Variability: A Truncated Normal Approach
Jinho Cha, Youngchul Kim, Junyeol Ryu +3
This study develops a strategic procurement framework integrating blockchain-based smart contracts with bounded demand variability modeled through a truncated normal distribution.…
Pigeon-SL: Robust Split Learning Framework for Edge Intelligence under Malicious Clients
Sangjun Park, Tony Q. S. Quek, Hyowoon Seo
Recent advances in split learning (SL) have established it as a promising framework for privacy-preserving, communication-efficient distributed learning at the network edge. Howeve…
Federated Learning Meets Fluid Antenna: Towards Robust and Scalable Edge Intelligence
Sangjun Park, Hyowoon Seo
Federated learning (FL) is an emerging machine learning paradigm with immense potential to support advanced services and applications in future industries. However, when deployed o…