activity
20242026
most citedMulti-Source Knowledge Pruning for Retrieval-Augmented Generation: A Benchmark and Empirical Study

3 citations · 3 across the 12 of their papers we have counts for

collaborators
Showing 2024Show all

2 papers · 1 filter

cs.IR2024

Revisiting the Solution of Meta KDD Cup 2024: CRAG

Jie Ouyang, Yucong Luo, Mingyue Cheng +4

This paper presents the solution of our team APEX in the Meta KDD CUP 2024: CRAG Comprehensive RAG Benchmark Challenge. The CRAG benchmark addresses the limitations of existing QA…

cs.CL2024★ 3 cited

Multi-Source Knowledge Pruning for Retrieval-Augmented Generation: A Benchmark and Empirical Study

Shuo Yu, Mingyue Cheng, Qi Liu +6

Retrieval-augmented generation (RAG) is increasingly recognized as an effective approach to mitigating the hallucination of large language models (LLMs) through the integration of…