most citedHyperCLOVA X Technical Report

7 citations · 7 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CV2025

Spanning Tree Autoregressive Visual Generation

Sangkyu Lee, Changho Lee, Janghoon Han +6

We present Spanning Tree Autoregressive (STAR) modeling, which can incorporate prior knowledge of images, such as center bias and locality, to maintain sampling performance while a…

cs.LG2025

KL Penalty Control via Perturbation for Direct Preference Optimization

Sangkyu Lee, Janghoon Han, Hosung Song +3

Direct Preference Optimization (DPO) demonstrates the advantage of aligning a large language model with human preference using only an offline dataset. However, DPO has the limitat…

cs.CV2025

MASS: Overcoming Language Bias in Image-Text Matching

Jiwan Chung, Seungwon Lim, Sangkyu Lee +1

Pretrained visual-language models have made significant advancements in multimodal tasks, including image-text retrieval. However, a major challenge in image-text matching lies in…

cs.AI2025

SEAL: Entangled White-box Watermarks on Low-Rank Adaptation

Giyeong Oh, Saejin Kim, Woohyun Cho +4

Recently, LoRA and its variants have become the de facto strategy for training and sharing task-specific versions of large pretrained models, thanks to their efficiency and simplic…

cs.CL2024★ 7 cited

HyperCLOVA X Technical Report

Kang Min Yoo, Jaegeun Han, Sookyo In +393

We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding. H…

cs.LG2024

Aligning Large Language Models by On-Policy Self-Judgment

Sangkyu Lee, Sungdong Kim, Ashkan Yousefpour +3

Existing approaches for aligning large language models with human preferences face a trade-off that requires a separate reward model (RM) for on-policy learning. In this paper, we…