3 papers
cs.CL2025
Evaluating Large Language Models on the 2026 Korean CSAT Mathematics Exam: Measuring Mathematical Ability in a Zero-Data-Leakage Setting
Goun Pyeon, Inbum Heo, Jeesu Jung +7
This study systematically evaluated the mathematical reasoning capabilities of Large Language Models (LLMs) using the 2026 Korean College Scholastic Ability Test (CSAT) Mathematics…
cs.LG2025
Reasoning Steps as Curriculum: Using Depth of Thought as a Difficulty Signal for Tuning LLMs
Jeesu Jung, Sangkeun Jung
Curriculum learning for training LLMs requires a difficulty signal that aligns with reasoning while remaining scalable and interpretable. We propose a simple premise: tasks that de…
cs.AI2025
ZEBRA: Leveraging Model-Behavioral Knowledge for Zero-Annotation Preference Dataset Construction
Jeesu Jung, Chanjun Park, Sangkeun Jung
Recent efforts in LLM alignment have focused on constructing large-scale preference datasets via human or Artificial Intelligence (AI) annotators. However, such approaches rely on…