2 citations · 2 across the 5 of their papers we have counts for
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cs.CL2025
Separate the Wheat from the Chaff: Winnowing Down Divergent Views in Retrieval Augmented Generation
Song Wang, Zihan Chen, Peng Wang +5
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by integrating external knowledge sources to address their limitations in accessing up-to-date or special…
cs.CL2024
Crafting Personalized Agents through Retrieval-Augmented Generation on Editable Memory Graphs
Zheng Wang, Zhongyang Li, Zeren Jiang +2
In the age of mobile internet, user data, often referred to as memories, is continuously generated on personal devices. Effectively managing and utilizing this data to deliver serv…
cs.CL2024★ 2 cited
M-RAG: Reinforcing Large Language Model Performance through Retrieval-Augmented Generation with Multiple Partitions
Zheng Wang, Shu Xian Teo, Jieer Ouyang +2
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by retrieving relevant memories from an external database. However, existing RAG methods typically organi…