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20242026
most citedGuidance-Based Prompt Data Augmentation in Specialized Domains for Named Entity Recognition

1 citations · 1 across the 8 of their papers we have counts for

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8 papers

cs.CV2026

LED: A Benchmark for Evaluating Layout Error Detection in Document Analysis

Inbum Heo, Taewook Hwang, Jeesu Jung +1

Recent advances in Large Language Models (LLMs) and Large Multimodal Models (LMMs) have improved Document Layout Analysis (DLA), yet structural errors such as region merging, split…

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.CY2025

What Drives Paper Acceptance? A Process-Centric Analysis of Modern Peer Review

Sangkeun Jung, Goun Pyeon, Inbum Heo +1

Peer review is the primary mechanism for evaluating scientific contributions, yet prior studies have mostly examined paper features or external metadata in isolation. The emergence…

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.CV2025

LED Benchmark: Diagnosing Structural Layout Errors for Document Layout Analysis

Inbum Heo, Taewook Hwang, Jeesu Jung +1

Recent advancements in Document Layout Analysis through Large Language Models and Multimodal Models have significantly improved layout detection. However, despite these improvement…

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…

cs.CL2024★ 1 cited

Guidance-Based Prompt Data Augmentation in Specialized Domains for Named Entity Recognition

Hyeonseok Kang, Hyein Seo, Jeesu Jung +3

While the abundance of rich and vast datasets across numerous fields has facilitated the advancement of natural language processing, sectors in need of specialized data types conti…

cs.LG2024

Exploring Domain Robust Lightweight Reward Models based on Router Mechanism

Hyuk Namgoong, Jeesu Jung, Sangkeun Jung +1

Recent advancements in large language models have heavily relied on the large reward model from reinforcement learning from human feedback for fine-tuning. However, the use of a si…