75 citations · 311 across the 28 of their papers we have counts for
4 papers · 1 filter
Making Text Embedders Few-Shot Learners
Chaofan Li, MingHao Qin, Shitao Xiao +5
Large language models (LLMs) with decoder-only architectures demonstrate remarkable in-context learning (ICL) capabilities. This feature enables them to effectively handle both fam…
Trustworthiness in Retrieval-Augmented Generation Systems: A Survey
Yujia Zhou, Wenbo Zhang, Jingying Shao +10
Retrieval-Augmented Generation (RAG) has quickly grown into a pivotal paradigm in the development of Large Language Models (LLMs). Although existing research mainly emphasizes accu…
A Multi-Task Embedder For Retrieval Augmented LLMs
Peitian Zhang, Shitao Xiao, Zheng Liu +2
LLMs confront inherent limitations in terms of its knowledge, memory, and action. The retrieval augmentation stands as a vital mechanism to address these limitations, which brings…
A Neural Corpus Indexer for Document Retrieval
Yujing Wang, Yingyan Hou, Haonan Wang +13
Current state-of-the-art document retrieval solutions mainly follow an index-retrieve paradigm, where the index is hard to be directly optimized for the final retrieval target. In…