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.AI2024
How Far Are We From AGI: Are LLMs All We Need?
Tao Feng, Chuanyang Jin, Jingyu Liu +5
The evolution of artificial intelligence (AI) has profoundly impacted human society, driving significant advancements in multiple sectors. AGI, distinguished by its ability to exec…