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Tao Qi

ACL 2026×4AAAI 2026×2ICML 2026KDD 2026检索与知识安全多模态数据与预训练评测与基准
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0 一作 · 8 篇 · ACL 2026 / AAAI 2026 / ICML 2026 / KDD 2026

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2026 顶会论文

8
作者位次 #2/10ACL 2026Poster· Natural Language Processing

Measuring Human Contribution in AI -Assisted Content Generation

Yueqi Xie · Tao Qi · Jingwei Yi · Xiyuan Yang · Ryan Whalen · Junming Huang · Qian Ding · Yu Xie · Xing Xie · Fangzhao Wu

摘要

With the growing prevalence of generative AI, an increasing amount of content is no longer exclusively generated by humans but by generative AI models with human guidance. This shift presents notable challenges for the delineation of originality due to the varying degrees of human contribution in...

评测与基准检索与知识社会与人本OpenReview 项目页
作者位次 #9/9ACL 2026Poster· Natural Language Processing

Knowledge Poisoning Attacks on Medical Multi-Modal Retrieval-Augmented Generation

Peiru Yang · Haoran Zheng · Tong Ju · Shiting Wang · Wanchun Ni · Jiajun Liu · Shangguang Wang · Yongfeng Huang · Tao Qi

摘要

Retrieval-augmented generation (RAG) is a widely adopted paradigm for enhancing LLMs in medical applications by incorporating expert multi-modal knowledge during generation. However, the underlying retrieval databases may naturally contain, or be intentionally injected with, adversarial knowledge...

检索与知识多模态安全OpenReview 项目页
作者位次 #9/9ACL 2026Poster· Natural Language Processing

Black-Box Membership Inference Attacks for Video Training Data in Multimodal Large Language Models

Jinrui Wang · Zhenfeng Gao · Wendan Wang · Huili Wang · Zichen Qin · Linjie Zhu · Hongke Fu · Shangguang Wang · Tao Qi

摘要

The increasing use of video data in training multimodal large language models (MLLMs) raises significant concerns on privacy leakage and copyright violations, highlighting the need for detecting improperly used training videos through membership inference attacks (MIAs). Most existing video MIA m...

多模态安全评测与基准OpenReview 项目页
作者位次 #9/9ACL 2026Poster· Natural Language Processing

Robust Membership Inference for Large Language Models under Adversarial Generative Corruption

Yuanhong Huang · Huili Wang · Xueying Bai · Jinrui Wang · Jiajun Liu · Ziqin Wang · Wanchun Ni · Shangguang Wang · Tao Qi

摘要

Membership inference attack (MIA) has emerged as a promising tool for auditing the training data of LLMs, supporting data privacy and copyright protection. Most existing MIA methods rely on the assumption that LLMs assign higher confidence scores to training samples than to non-training ones.Howe...

安全效率与系统数据与预训练OpenReview 项目页

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