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Eunho Yang

ICLR 2026×5ACL 2026×3CVPR 2026多模态数据与预训练安全社会与人本强化学习与后训练
新星分资深
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一作贡献0
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方向广度16
合作网络6
0 一作 · 9 篇 · ICLR 2026 / ACL 2026 / CVPR 2026

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2026 顶会论文

9
作者位次 #5/5ICLR 2026Poster· Reinforcement Learning / Deep RL

No Prompt Left Behind: Exploiting Zero-Variance Prompts in LLM Reinforcement Learning via Entropy-Guided Advantage Shaping

Thanh-Long V. Le · Myeongho Jeon · Kim Vu · Viet Lai · Eunho Yang

强化学习与后训练OpenReview
作者位次 #5/5ICLR 2026Poster· Social Aspects / Privacy-preserving Statistics and Machine Learning

Secure Inference for Diffusion Models via Unconditional Scores

Jaeyun Song · Geondo Park · Uigyu Kim · Joonhyung Park · Eunho Yang

安全效率与系统社会与人本OpenReview
作者位次 #4/4ICLR 2026Poster· Applications / Language, Speech and Dialog

When to Ensemble: Identifying Token-Level Points for Stable and Fast LLM Ensembling

Heecheol Yun · Kwangmin Ki · Jung Hyun Lee · Eunho Yang

多模态OpenReview
作者位次 #4/4ACL 2026Poster· Natural Language Processing

Bringing Real-World Relations into Video Generation with Graph-Structured Knowledge

Joonhyung Park · Jaeyun Song · Sihwan Park · Eunho Yang

摘要

Recent proprietary video generation models have demonstrated remarkable proficiency in synthesizing highly realistic videos from textual instructions. Most open-source text-to-video models, however, still struggle to accurately simulate real-world physics and dynamic entity interactions. Existing...

数据与预训练可解释性对齐与偏好OpenReview 项目页
作者位次 #3/3ACL 2026Findings· Findings of ACL

CUR a TE : Continual Unlearning in Real Time with Ensured Preservation of LLM Knowledge

Seyun Bae · Seokhan Lee · Eunho Yang

摘要

The inability to filter out in advance all potentially problematic data from the pre-training of large language models has given rise to the need for methods for unlearning specific pieces of knowledge after training. Existing techniques overlook the need for continuous and immediate action, caus...

数据与预训练安全检索与知识OpenReview 项目页
作者位次 #7/7ACL 2026Findings· Findings of ACL

P hase MI : A Motivational Interviewing Dataset for Enhancing Phase Progression in LLM -based Counseling

Jina Kim · Myeongho Jeon · Soohyun Cho · Chae-Gyun Lim · Jongmin Lim · Haewon Min · Eunho Yang

摘要

The growing demand for scalable mental health support has increased interest in AI-based counseling systems grounded in Motivational Interviewing (MI). However, existing MI datasets do not explicitly model the structured progression of MI phases, which is essential for effective and goal-oriented...

评测与基准数据与预训练社会与人本OpenReview 项目页

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