15 citations · 17 across the 20 of their papers we have counts for
4 papers · 1 filter
Doc-REFRAG: Rethinking Multimodal Document Retrieval-Augmented Generation
Ruofan Hu, Shengyang Xu, Minjie Hong +5
Real-world knowledge resides in multimodal documents, necessitating retrieval-augmented generation (RAG) for accurate question answering. However, existing multimodal RAG models ar…
Vela: Scalable Embeddings with Voice Large Language Models for Multimodal Retrieval
Ruofan Hu, Yan Xia, Minjie Hong +5
Multimodal large language models (MLLMs) have seen substantial progress in recent years. However, their ability to represent multimodal information in the acoustic domain remains u…
EAGER-LLM: Enhancing Large Language Models as Recommenders through Exogenous Behavior-Semantic Integration
Minjie Hong, Yan Xia, Zehan Wang +8
Large language models (LLMs) are increasingly leveraged as foundational backbones in the development of advanced recommender systems, offering enhanced capabilities through their e…
CART: A Generative Cross-Modal Retrieval Framework with Coarse-To-Fine Semantic Modeling
Minghui Fang, Shengpeng Ji, Jialong Zuo +9
Cross-modal retrieval aims to search for instances, which are semantically related to the query through the interaction of different modal data. Traditional solutions utilize a sin…