3 papers
cs.CL2026
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.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.CL2024
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…