1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.CL2026
Tackling the Inherent Difficulty of Noise Filtering in RAG
Jingyu Liu, Jiaen Lin, Yong Liu
Retrieval-Augmented Generation (RAG) has become a widely adopted approach to enhance Large Language Models (LLMs) by incorporating external knowledge and reducing hallucinations. H…
cs.CL2025
Optimizing Multi-Hop Document Retrieval Through Intermediate Representations
Jiaen Lin, Jingyu Liu, Yingbo Liu
Retrieval-augmented generation (RAG) encounters challenges when addressing complex queries, particularly multi-hop questions. While several methods tackle multi-hop queries by iter…
cs.CL2024★ 1 cited
How Much Can RAG Help the Reasoning of LLM?
Jingyu Liu, Jiaen Lin, Yong Liu
Retrieval-Augmented Generation (RAG) has gained significant popularity in modern Large Language Models (LLMs) due to its effectiveness in introducing new knowledge and reducing hal…