19 citations · 70 across the 32 of their papers we have counts for
35 papers
HVM-GraphRAG: Holistic-View Multimodal Graph Retrieval-Augmented Generation on Complex Document
Xin He, Yili Wang, Wenqi Fan +4
Question answering (QA) over complex documents requires models to retrieve and integrate evidence distributed across distant document regions and modalities. Multimodal GraphRAG pr…
Learning Spatio-Temporal Foundation Models from Pure Synthetic Data
Yutong Feng, Shiyuan Piao, Yutong Xia +5
Spatio-Temporal Foundation Models (STFMs) aim to learn generalizable representations of complex dynamical systems across space and time. However, existing approaches suffer from di…
CFALR: Collaborative Filtering-Augmented Large Language Model for Personalized Fashion Outfit Recommendation
Yujuan Ding, Junrong Liao, Yunshan Ma +4
Personalized outfit recommendation poses a significant challenge in e-commerce and social media platforms, requiring systems that balance user preferences with aesthetic compatibil…
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…
Double-Calibration: Towards Reliable LLMs via Calibrating Knowledge and Reasoning Confidence
Yuyin Lu, Ziran Liang, Yanghui Rao +3
Reliable reasoning in Large Language Models (LLMs) is challenged by their propensity for hallucination. While augmenting LLMs with Knowledge Graphs (KGs) improves factual accuracy,…
DeMa: Dual-Path Delay-Aware Mamba for Efficient Multivariate Time Series Analysis
Rui An, Haohao Qu, Wenqi Fan +2
Accurate and efficient multivariate time series (MTS) analysis is increasingly critical for a wide range of intelligent applications. Within this realm, Transformers have emerged a…
WebRec: Enhancing LLM-based Recommendations with Attention-guided RAG from Web
Zihuai Zhao, Yujuan Ding, Wenqi Fan +1
Recommender systems play a vital role in alleviating information overload and enriching users' online experience. In the era of large language models (LLMs), LLM-based recommender…
Continuous-time Discrete-space Diffusion Model for Recommendation
Chengyi Liu, Xiao Chen, Shijie Wang +2
In the era of information explosion, Recommender Systems (RS) are essential for alleviating information overload and providing personalized user experiences. Recent advances in dif…
Benchmarking for Domain-Specific LLMs: A Case Study on Academia and Beyond
Rubing Chen, Jiaxin Wu, Jian Wang +5
The increasing demand for domain-specific evaluation of large language models (LLMs) has led to the development of numerous benchmarks. These efforts often adhere to the principle…
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…
QA-Dragon: Query-Aware Dynamic RAG System for Knowledge-Intensive Visual Question Answering
Zhuohang Jiang, Pangjing Wu, Xu Yuan +2
Retrieval-Augmented Generation (RAG) has been introduced to mitigate hallucinations in Multimodal Large Language Models (MLLMs) by incorporating external knowledge into the generat…
Damba-ST: Domain-Adaptive Mamba for Efficient Urban Spatio-Temporal Prediction
Rui An, Yifeng Zhang, Ziran Liang +4
Training urban spatio-temporal foundation models that generalize well across diverse regions and cities is critical for deploying urban services in unseen or data-scarce regions. R…
GLProtein: Global-and-Local Structure Aware Protein Representation Learning
Yunqing Liu, Wenqi Fan, Xiaoyong Wei +1
Proteins are central to biological systems, participating as building blocks across all forms of life. Despite advancements in understanding protein functions through protein seque…
Investigating and Mitigating Stereotype-aware Unfairness in LLM-based Recommendations
Zihuai Zhao, Wenqi Fan, Yao Wu +1
Large Language Models (LLMs) have demonstrated unprecedented language understanding and reasoning capabilities to capture diverse user preferences and advance personalized recommen…
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…
SemDiff: Generating Natural Unrestricted Adversarial Examples via Semantic Attributes Optimization in Diffusion Models
Zeyu Dai, Shengcai Liu, Rui He +5
Unrestricted adversarial examples (UAEs), allow the attacker to create non-constrained adversarial examples without given clean samples, posing a severe threat to the safety of dee…
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…
iTFKAN: Interpretable Time Series Forecasting with Kolmogorov-Arnold Network
Ziran Liang, Rui An, Wenqi Fan +2
As time evolves, data within specific domains exhibit predictability that motivates time series forecasting to predict future trends from historical data. However, current deep for…
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…
Computational Protein Science in the Era of Large Language Models (LLMs)
Wenqi Fan, Yi Zhou, Shijie Wang +5
Considering the significance of proteins, computational protein science has always been a critical scientific field, dedicated to revealing knowledge and developing applications wi…
Knowledge Graph Retrieval-Augmented Generation for LLM-based Recommendation
Shijie Wang, Wenqi Fan, Yue Feng +4
Recommender systems have become increasingly vital in our daily lives, helping to alleviate the problem of information overload across various user-oriented online services. The em…
ChartAdapter: Large Vision-Language Model for Chart Summarization
Peixin Xu, Yujuan Ding, Wenqi Fan
Chart summarization, which focuses on extracting key information from charts and interpreting it in natural language, is crucial for generating and delivering insights through effe…