121 citations · 210 across the 17 of their papers we have counts for
5 papers · 1 filter
Benchmarking and Defending Against Indirect Prompt Injection Attacks on Large Language Models
Jingwei Yi, Yueqi Xie, Bin Zhu +4
The integration of large language models with external content has enabled applications such as Microsoft Copilot but also introduced vulnerabilities to indirect prompt injection a…
Exploring Recommendation Capabilities of GPT-4V(ision): A Preliminary Case Study
Peilin Zhou, Meng Cao, You-Liang Huang +6
Large Multimodal Models (LMMs) have demonstrated impressive performance across various vision and language tasks, yet their potential applications in recommendation tasks with visu…
LLMRec: Benchmarking Large Language Models on Recommendation Task
Junling Liu, Chao Liu, Peilin Zhou +8
Recently, the fast development of Large Language Models (LLMs) such as ChatGPT has significantly advanced NLP tasks by enhancing the capabilities of conversational models. However,…
Attention Calibration for Transformer-based Sequential Recommendation
Peilin Zhou, Qichen Ye, Yueqi Xie +5
Transformer-based sequential recommendation (SR) has been booming in recent years, with the self-attention mechanism as its key component. Self-attention has been widely believed t…
Rethinking Multi-Interest Learning for Candidate Matching in Recommender Systems
Yueqi Xie, Jingqi Gao, Peilin Zhou +5
Existing research efforts for multi-interest candidate matching in recommender systems mainly focus on improving model architecture or incorporating additional information, neglect…