19 citations · 45 across the 10 of their papers we have counts for
12 papers
Inference Cost Attacks for Retrieval-Augmented Large Language Models
Chengliang Liu, Liangbo Ning, Yujuan Ding +1
Retrieval-Augmented Generation (RAG)-enhanced LLM systems, while powerful, introduce substantial inference costs due to the inclusion of an extra multi-stage pipeline that dynamica…
ReRec: Reasoning-Augmented LLM-based Recommendation Assistant via Reinforcement Fine-tuning
Jiani Huang, Shijie Wang, Liangbo Ning +2
With the rise of LLMs, there is an increasing need for intelligent recommendation assistants that can handle complex queries and provide personalized, reasoning-driven recommendati…
mKG-RAG: Leveraging Multimodal Knowledge Graphs in Retrieval-Augmented Generation for Knowledge-intensive VQA
Xu Yuan, Liangbo Ning, Qingqing Ye +2
Retrieval-Augmented Generation (RAG) has emerged as an effective paradigm for expanding the knowledge capacity of Multimodal Large Language Models (MLLMs) by incorporating external…
CheatAgent: Attacking LLM-Empowered Recommender Systems via LLM Agent
Liang-bo Ning, Shijie Wang, Wenqi Fan +4
Recently, Large Language Model (LLM)-empowered recommender systems (RecSys) have brought significant advances in personalized user experience and have attracted considerable attent…
Exploring Backdoor Attack and Defense for LLM-empowered Recommendations
Liangbo Ning, Wenqi Fan, Qing Li
The fusion of Large Language Models (LLMs) with recommender systems (RecSys) has dramatically advanced personalized recommendations and drawn extensive attention. Despite the impre…
Retrieval-Augmented Purifier for Robust LLM-Empowered Recommendation
Liangbo Ning, Wenqi Fan, Qing Li
Recently, Large Language Model (LLM)-empowered recommender systems have revolutionized personalized recommendation frameworks and attracted extensive attention. Despite the remarka…
A Survey of WebAgents: Towards Next-Generation AI Agents for Web Automation with Large Foundation Models
Liangbo Ning, Ziran Liang, Zhuohang Jiang +8
With the advancement of web techniques, they have significantly revolutionized various aspects of people's lives. Despite the importance of the web, many tasks performed on it are…
Towards Next-Generation Recommender Systems: A Benchmark for Personalized Recommendation Assistant with LLMs
Jiani Huang, Shijie Wang, Liang-bo Ning +4
Recommender systems (RecSys) are widely used across various modern digital platforms and have garnered significant attention. Traditional recommender systems usually focus only on…
SSD4Rec: A Structured State Space Duality Model for Efficient Sequential Recommendation
Haohao Qu, Yifeng Zhang, Liangbo Ning +2
Sequential recommendation methods are crucial in modern recommender systems for their remarkable capability to understand a user's changing interests based on past interactions. Ho…
Joint Universal Adversarial Perturbations with Interpretations
Liang-bo Ning, Zeyu Dai, Wenqi Fan +4
Deep neural networks (DNNs) have significantly boosted the performance of many challenging tasks. Despite the great development, DNNs have also exposed their vulnerability. Recent…
A Survey of Mamba
Haohao Qu, Liangbo Ning, Rui An +5
As one of the most representative DL techniques, Transformer architecture has empowered numerous advanced models, especially the large language models (LLMs) that comprise billions…
A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models
Wenqi Fan, Yujuan Ding, Liangbo Ning +5
As one of the most advanced techniques in AI, Retrieval-Augmented Generation (RAG) can offer reliable and up-to-date external knowledge, providing huge convenience for numerous tas…