19 citations · 45 across the 17 of their papers we have counts for
21 papers
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…
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…