most citedM-RAG: Reinforcing Large Language Model Performance through Retrieval-Augmented Generation with Multiple Partitions

2 citations · 2 across the 3 of their papers we have counts for

collaborators

3 papers

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.AI2025

MASTER: A Multi-Agent System with LLM Specialized MCTS

Bingzheng Gan, Yufan Zhao, Tianyi Zhang +5

Large Language Models (LLM) are increasingly being explored for problem-solving tasks. However, their strategic planning capability is often viewed with skepticism. Recent studies…

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…