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

Zhenbo Xu

ACL 2026×2ECCV 2026×2多模态对齐与偏好可解释性效率与系统数据与预训练
新星分贡献者
57
一作贡献0
核心/参与27
方向广度13
合作网络6
0 一作 · 4 篇 · ACL 2026 / ECCV 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

4
作者位次 #5/8ACL 2026Poster· Natural Language Processing

Interpretable Safety Alignment via SAE -Constructed Low-Rank Subspace Adaptation

Dianyun Wang · Qingsen Ma · Yuhu Shang · Zhifeng Lu · Zhenbo Xu · Lechen Ning · Huijia Wu · Zhaofeng He

摘要

Safety alignment—training large language models (LLMs) to refuse harmful requests while remaining helpful—is critical for responsible deployment. Prior work established that safety behaviors are governed by low-rank structures, suggesting parameter-efficient fine-tuning (PEFT) should be well-suit...

可解释性效率与系统对齐与偏好OpenReview 项目页
作者位次 #5/7ACL 2026Poster· Natural Language Processing

SCVQ : Sparse-Compensated Vector Quantization for Large Language Models

Zixuan Zhou · Yujun Diao · Zicheng Kong · Dehua Ma · Zhenbo Xu · Pei Pei Li · Zhaofeng He

摘要

Large Language Models (LLMs) are primarily constrained by memory and bandwidth bottlenecks during deployment. Although Vector Quantization (VQ) has emerged as a promising solution, existing methods incur inference overhead due to massive codebook storage and intensive index lookups. Moreover, the...

效率与系统可解释性评测与基准OpenReview 项目页

常见合作者