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

Tao Wei

CCS 2026×3ACL 2026×2NDSS 2026VLDB 2026安全代码效率与系统检索与知识评测与基准
新星分资深
43
一作贡献0
核心/参与40
方向广度19
合作网络12
0 一作 · 7 篇 · CCS 2026 / ACL 2026 / NDSS 2026 / VLDB 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

7
作者位次 #5/7ACL 2026Poster· Natural Language Processing

Revisiting the Reliability of Language Models in Instruction-Following

Jianshuo Dong · Yutong Zhang · Liu Yan · Zhenyu Zhong · Tao Wei · Chao Zhang · Han Qiu

摘要

Advanced LLMs have achieved near-ceiling instruction-following accuracy on benchmarks such as IFEval. However, these impressive scores do not necessarily translate to reliable services in real-world use, where users often vary their phrasing, contextual framing, and task formulations. In this pap...

评测与基准代码多语言与语言学OpenReview 项目页
作者位次 #6/8ACL 2026Findings· Findings of ACL

C o T rust: Privacy-Preserving Collaboration Between Large and Small Language Models in Trusted Execution Environments

Zhenya Ma · Tingyi Wang · Yongheng Deng · Ziqing Qiao · Yinggui Wang · Tao Wei · Lei Wang · Ju Ren

摘要

Services powered by large language models (LLMs) provide powerful text generation capabilities, but accessing sensitive user inputs raises serious privacy concerns. Trusted Execution Environments (TEEs) provide a secure computation environment, enabling sensitive inputs to be safely processed. Ho...

效率与系统检索与知识推理OpenReview 项目页

常见合作者