6 papers
Visual Prompt Discovery via Semantic Exploration
Jaechang Kim, Yotaro Shimose, Zhao Wang +3
LVLMs encounter significant challenges in image understanding and visual reasoning, leading to critical perception failures. Visual prompts, which incorporate image manipulation co…
Towards Faithful Agentic XAI: A Verification Method and an Open-World Benchmark for Better Model Faithfulness
Jaechang Kim, Sunung Mun, Seungjoon Lee +2
Explainable AI (XAI) helps users interpret model behavior and identify potential faults. Agentic XAI systems use Large Language Models (LLMs) to make explanations more accessible t…
MMTB: Evaluating Terminal Agents on Multimedia-File Tasks
Chiyeong Heo, Jaechang Kim, Junhyuk Kwon +4
Terminals provide a powerful interface for AI agents by exposing diverse tools for automating complex workflows, yet existing terminal-agent benchmarks largely focus on tasks groun…
Active Prompt Learning with Vision-Language Model Priors
Hoyoung Kim, Seokhee Jin, Changhwan Sung +2
Vision-language models (VLMs) have demonstrated remarkable zero-shot performance across various classification tasks. Nonetheless, their reliance on hand-crafted text prompts for e…
Semantic Exploration with Adaptive Gating for Efficient Problem Solving with Language Models
Sungjae Lee, Hyejin Park, Jaechang Kim +1
Recent advancements in large language models (LLMs) have shown remarkable potential in various complex tasks requiring multi-step reasoning methods like tree search to explore dive…
Bridging the Gap between Expert and Language Models: Concept-guided Chess Commentary Generation and Evaluation
Jaechang Kim, Jinmin Goh, Inseok Hwang +2
Deep learning-based expert models have reached superhuman performance in decision-making domains such as chess and Go. However, it is under-explored to explain or comment on given…