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Hang Ding · Dongqi Liu · Qiming Feng · Jian Li · Tong Lei · Jiafu Wu · Shuo Wang · Jiangning Zhang · Chengjie Wang · Yabiao Wang
Dongqi Liu · Hang Ding · Qiming Feng · Xurong Xie · Zhucun Xue · Chengjie Wang · Jian Li · Jiangning Zhang · Yabiao Wang
Retrieval-Augmented Generation (RAG) has emerged as an important means of enhancing the performance of large language models (LLMs) in knowledge-intensive tasks. However, most existing RAG strategies treat retrieved passages in a flat and unstructured way, which prevents the model from capturing...
Jiawei Zhou · Hang Ding · Haiyun Jiang
Retrieval-Augmented Generation (RAG) has emerged as a powerful framework for knowledge-intensive tasks, yet its effectiveness in long-context scenarios is often bottlenecked by the retriever’s inability to distinguish sparse yet crucial evidence. Standard retrievers, optimized for query-document...