3 citations · 4 across the 6 of their papers we have counts for
4 papers · 1 filter
Learning Refined Document Representations for Dense Retrieval via Deliberate Thinking
Yifan Ji, Zhipeng Xu, Zhenghao Liu +7
Recent dense retrievers increasingly leverage the robust text understanding capabilities of Large Language Models (LLMs), encoding queries and documents into a shared embedding spa…
ExpandR: Teaching Dense Retrievers Beyond Queries with LLM Guidance
Sijia Yao, Pengcheng Huang, Zhenghao Liu +4
Large language models (LLMs) have demonstrated significant potential in enhancing dense retrieval through query augmentation. However, most existing methods treat the LLM and the r…
UltraRAG: A Modular and Automated Toolkit for Adaptive Retrieval-Augmented Generation
Yuxuan Chen, Dewen Guo, Sen Mei +12
Retrieval-Augmented Generation (RAG) significantly enhances the performance of large language models (LLMs) in downstream tasks by integrating external knowledge. To facilitate res…
VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality Documents
Shi Yu, Chaoyue Tang, Bokai Xu +8
Retrieval-augmented generation (RAG) is an effective technique that enables large language models (LLMs) to utilize external knowledge sources for generation. However, current RAG…