10 citations · 17 across the 25 of their papers we have counts for
9 papers · 1 filter
TeaRAG: A Token-Efficient Agentic Retrieval-Augmented Generation Framework
Chao Zhang, Yuhao Wang, Derong Xu +9
Retrieval-Augmented Generation (RAG) utilizes external knowledge to augment Large Language Models' (LLMs) reliability. For flexibility, agentic RAG employs autonomous, multi-round…
ImageScope: Unifying Language-Guided Image Retrieval via Large Multimodal Model Collective Reasoning
Pengfei Luo, Jingbo Zhou, Tong Xu +3
With the proliferation of images in online content, language-guided image retrieval (LGIR) has emerged as a research hotspot over the past decade, encompassing a variety of subtask…
ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval
Suyuan Huang, Chao Zhang, Yuanyuan Wu +12
Dense retrieval in most industries employs dual-tower architectures to retrieve query-relevant documents. Due to online deployment requirements, existing real-world dense retrieval…
When Box Meets Graph Neural Network in Tag-aware Recommendation
Fake Lin, Ziwei Zhao, Xi Zhu +6
Last year has witnessed the re-flourishment of tag-aware recommender systems supported by the LLM-enriched tags. Unfortunately, though large efforts have been made, current solutio…
DynLLM: When Large Language Models Meet Dynamic Graph Recommendation
Ziwei Zhao, Fake Lin, Xi Zhu +6
Last year has witnessed the considerable interest of Large Language Models (LLMs) for their potential applications in recommender systems, which may mitigate the persistent issue o…
NoteLLM-2: Multimodal Large Representation Models for Recommendation
Chao Zhang, Haoxin Zhang, Shiwei Wu +6
Large Language Models (LLMs) have demonstrated exceptional proficiency in text understanding and embedding tasks. However, their potential in multimodal representation, particularl…