paper.dinq.meAI 与前沿科学顶会 · 学术新星雷达
登录 DINQ
返回新星榜
JO

Jie Ou

ACL 2026×2可解释性安全对齐与偏好效率与系统长上下文与架构
新星分新星
58
一作贡献30
核心/参与5
方向广度10
合作网络3
1 一作 · 2 篇 · ACL 2026

获取完整研究档案

首次获取该候选人的完整档案及 StartLux 评估共 20 积分;每位用户单独解锁,之后查看免费。主动刷新 20 积分。

联系方式

实时查找电子邮箱,费用不超过20积分;未找到邮箱不扣费。

2026 顶会论文

2
作者位次 #7/10ACL 2026Poster· Natural Language Processing

CAP : Controllable Alignment Prompting for Unlearning in LLM s

Zhaokun Wang · Jinyu Guo · Jingwen Pu · Hongli Pu · Meng Yang · Xunlei Chen · Jie Ou · Wenyi Li · Guangchun Luo · Wenhong Tian

摘要

Large language models (LLMs) trained on unfiltered corpora inherently risk retaining sensitive information, necessitating selective knowledge unlearning for regulatory compliance and ethical safety. However, existing parameter-modifying methods face fundamental limitations: high computational cos...

安全对齐与偏好可解释性OpenReview 项目页
一作ACL 2026Poster· Natural Language Processing

A dap S hot: Adaptive Many-Shot In-Context Learning with Semantic-Aware KV Cache Reuse

Jie Ou · Jinyu Guo · Shiyao Guo · Yuang Li · Ruiqi Wu · Zhaokun Wang · Wenyi Li · Wenhong Tian

摘要

Many-Shot In-Context Learning (ICL) has emerged as a promising paradigm, leveraging extensive examples to unlock the reasoning potential of Large Language Models (LLMs). However, existing methods typically rely on a predetermined, fixed number of shots. This static approach often fails to adapt t...

可解释性效率与系统长上下文与架构OpenReview 项目页

常见合作者