3 citations · 3 across the 8 of their papers we have counts for
8 papers
Solar Open 2 Technical Report
Sungrae Park, Sanghoon Kim, Gyoungjin Gim +50
We present Solar Open 2, a 250B-A15B Mixture-of-Experts language model built for long-horizon agentic tasks, scaled up from Solar Open 1 (Solar Open 100B). To hold entire agent tra…
Enhancing Multi-Image Understanding through Delimiter Token Scaling
Minyoung Lee, Yeji Park, Dongjun Hwang +3
Large Vision-Language Models (LVLMs) achieve strong performance on single-image tasks, but their performance declines when multiple images are provided as input. One major reason i…
Knowledge Vector Weakening: Efficient Training-free Unlearning for Large Vision-Language Models
Yejin Kim, Dongjun Hwang, Sungmin Cha +1
Large Vision-Language Models (LVLMs) are widely adopted for their strong multimodal capabilities, yet they raise serious concerns such as privacy leakage and harmful content genera…
Solar Open Technical Report
Sungrae Park, Sanghoon Kim, Jungho Cho +34
We introduce Solar Open, a 102B-parameter bilingual Mixture-of-Experts language model for underserved languages. Solar Open demonstrates a systematic methodology for building compe…
Improving Fisher Information Estimation and Efficiency for LoRA-based LLM Unlearning
Yejin Kim, Eunwon Kim, Buru Chang +1
LLMs have demonstrated remarkable performance across various tasks but face challenges related to unintentionally generating outputs containing sensitive information. A straightfor…
Mitigating Cross-Image Information Leakage in Multi-Image Understanding with Large Vision-Language Models
Yeji Park, Minyoung Lee, Sanghyuk Chun +1
Large Vision-Language Models (LVLMs) exhibit strong performance on single-image tasks. However, their performance degrades significantly when handling multi-image inputs. While thi…
LMLT: Low-to-high Multi-Level Vision Transformer for Image Super-Resolution
Jeongsoo Kim, Jongho Nang, Junsuk Choe
Recent Vision Transformer (ViT)-based methods for Image Super-Resolution have demonstrated impressive performance. However, they suffer from significant complexity, resulting in hi…
ConVis: Contrastive Decoding with Hallucination Visualization for Mitigating Hallucinations in Multimodal Large Language Models
Yeji Park, Deokyeong Lee, Junsuk Choe +1
Hallucinations in Multimodal Large Language Models (MLLMs) where generated responses fail to accurately reflect the given image pose a significant challenge to their reliability. T…