3 citations · 3 across the 6 of their papers we have counts for
12 papers · 1 filter
SEEK: Steering LLM Reasoning for RAG via Internal Reasoning Sketches
Xinze Li, Yuqing Lan, Zhenghao Liu +7
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by incorporating external knowledge into the generation process. Benefiting from the reasoning capabiliti…
Finding What Matters: Anchoring Context Knowledge with Evolving Indices for Iterative Retrieval
Mingyan Wu, Zhenghao Liu, Xinze Li +7
Retrieval-Augmented Generation (RAG) has become a dominant paradigm for mitigating hallucinations in Large Language Models (LLMs) by incorporating external knowledge. However, exis…
NaviRAG: Towards Active Knowledge Navigation for Retrieval-Augmented Generation
Jihao Dai, Dingjun Wu, Yuxuan Chen +4
Retrieval-augmented generation (RAG) typically relies on a flat retrieval paradigm that maps queries directly to static, isolated text segments. This approach struggles with more c…
Scientific Knowledge-driven Decoding Constraints Improving the Reliability of LLMs
Maotian Ma, Zheni Zeng, Zhenghao Liu +1
Large language models (LLMs) have shown strong knowledge reserves and task-solving capabilities, but still face the challenge of severe hallucination, hindering their practical app…
ThinkNote: Enhancing Knowledge Integration and Utilization of Large Language Models via Constructivist Cognition Modeling
Zhipeng Xu, Zhenghao Liu, Yukun Yan +7
Large Language Models (LLMs) have demonstrated strong performance across a wide range of NLP tasks. However, they often exhibit suboptimal behaviors and inconsistencies when expose…
KARE-RAG: Knowledge-Aware Refinement and Enhancement for RAG
Yongjian Li, HaoCheng Chu, Yukun Yan +7
Retrieval-Augmented Generation (RAG) enables large language models (LLMs) to access broader knowledge sources, yet factual inconsistencies persist due to noise in retrieved documen…