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The Atomic Instruction Gap: Instruction-Tuned LLMs Struggle with Simple, Self-Contained Directives
Henry Lim, Kwan Hui Lim
Instruction-tuned large language models (IT-LLMs) exhibit strong zero-shot reasoning, yet their ability to execute simple, self-contained instructions remains underexplored, despit…
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…