2 citations · 3 across the 6 of their papers we have counts for
5 papers · 1 filter
Balancing Accuracy and Efficiency in Multi-Turn Intent Classification for LLM-Powered Dialog Systems in Production
Junhua Liu, Yong Keat Tan, Bin Fu +1
Accurate multi-turn intent classification is essential for advancing conversational AI systems. However, challenges such as the scarcity of comprehensive datasets and the complexit…
Towards Objective and Unbiased Decision Assessments with LLM-Enhanced Hierarchical Attention Networks
Junhua Liu, Kwan Hui Lim, Roy Ka-Wei Lee
How objective and unbiased are we while making decisions? This work investigates cognitive bias identification in high-stake decision making process by human experts, questioning i…
Understanding Fairness-Accuracy Trade-offs in Machine Learning Models: Does Promoting Fairness Undermine Performance?
Junhua Liu, Roy Ka-Wei Lee, Kwan Hui Lim
Fairness in both Machine Learning (ML) predictions and human decision-making is essential, yet both are susceptible to different forms of bias, such as algorithmic and data-driven…
Physics-Informed Autonomous LLM Agents for Explainable Power Electronics Modulation Design
Junhua Liu, Fanfan Lin, Xinze Li +2
LLM-based autonomous agents have recently shown strong capabilities in solving complex industrial design tasks. However, in domains aiming for carbon neutrality and high-performanc…
LARA: Linguistic-Adaptive Retrieval-Augmentation for Multi-Turn Intent Classification
Junhua Liu, Yong Keat Tan, Bin Fu +1
Multi-turn intent classification is notably challenging due to the complexity and evolving nature of conversational contexts. This paper introduces LARA, a Linguistic-Adaptive Retr…