4 papers
When Quantization Preserves Accuracy but Not Evidence: Explanation-Aware Post-Training Quantization for Medical LLMs
Yeji Kim, Mi-Young Kim, Randy Goebel
Post-training quantization (PTQ) enables efficient deployment of large language models, and PTQ methods are usually optimized and evaluated with generic reconstruction, perplexity,…
Does Role Specialization Matter for Explanation Faithfulness in Mixture-of-Experts?
Yeji Kim, Housam Babiker, Mi-Young Kim +1
Mixture-of-Experts (MoE) architectures have recently been extended with role-based mechanisms for interpretability. This is typically done by assigning semantic roles to individual…
An LLM-based Chain-of-Response Counter-Scam System
Heedou Kim, Mogan Gim, Donghee Choi +4
The rapid evolution of online scams, driven by transnational networks and mass produced social engineering scenarios, has exposed the speed limitations of conventional detection, n…
Feature-level Interaction Explanations in Multimodal Transformers
Yeji Kim, Housam Khalifa Bashier Babiker, Mi-Young Kim +1
Multimodal Transformers often produce predictions without clarifying how different modalities jointly support a decision. Most existing multimodal explainable AI (MXAI) methods ext…