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Byung-Jun Lee

ACL 2026×2ICLR 2026×2AAAI 2026CVPR 2026多模态检索与知识效率与系统对齐与偏好社会与人本
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方向广度19
合作网络6
0 一作 · 6 篇 · ACL 2026 / ICLR 2026 / AAAI 2026 / CVPR 2026

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2026 顶会论文

6
作者位次 #4/4ICLR 2026Poster· Applications / Language, Speech and Dialog

Beyond RAG vs. Long-Context: Learning Distraction-Aware Retrieval for Efficient Knowledge Grounding

Seongwoong Shim · Myunsoo Kim · Jae Cho · Byung-Jun Lee

检索与知识多模态效率与系统OpenReview
作者位次 #3/3ICLR 2026Poster· Deep Learning / Algorithms

SCAD: Super-Class-Aware Debiasing for Long-Tailed Semi-Supervised Learning

Sunguk Jang · Jinwoo Jeon · Byung-Jun Lee

社会与人本OpenReview
作者位次 #3/3CVPR 2026Poster· Computer Vision & Pattern Recognition

FALCON: False-Negative Aware Learning of Contrastive Negatives in Vision-Language Alignment

Myunsoo Kim · Seongwoong Shim · Byung-Jun Lee

对齐与偏好多模态OpenReview 项目页
作者位次 #3/5ACL 2026Poster· Natural Language Processing

SGT : Securing Open-Source LLM s Against Malicious Fine-tuning via Safety Guidance Trigger

Sunguk Shin · Fangzhao Wu · Byung-Jun Lee · Meeyoung Cha · Sungwon Park

摘要

Open-weight large language models (LLMs) enable broad customization, but also increase exposure to post-release misuse, including malicious fine-tuning (MFT). To mitigate this risk, many prior defenses aim to improve the robustness of open-weight models to MFT by constraining adversarial fine-tun...

安全对齐与偏好可解释性OpenReview 项目页
作者位次 #7/7ACL 2026Findings· Findings of ACL

G raph M ind: LLM s as Dynamic Knowledge Builders for Sequential Decision-Making

Sunguk Shin · Hayeong Lee · Jun Ho Seo · Jinho Lee · Myunsoo Kim · Minsuk Chang · Byung-Jun Lee

摘要

While the reasoning capabilities of large language models (LLMs) have advanced considerably, efficiently internalizing and leveraging new information in dynamically interactive environments remains a significant challenge. This limitation is particularly pronounced in partially observable environ...

检索与知识推理智能体与工具OpenReview 项目页

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