1 citations · 1 across the 9 of their papers we have counts for
4 papers · 1 filter
Reinforced Information Retrieval
Chaofan Li, Zheng Liu, Jianlyv Chen +2
While retrieval techniques are widely used in practice, they still face significant challenges in cross-domain scenarios. Recently, generation-augmented methods have emerged as a p…
O1 Embedder: Let Retrievers Think Before Action
Ruiran Yan, Zheng Liu, Defu Lian
The growing power of large language models (LLMs) has revolutionized how people access and utilize information. Notably, the LLMs excel at performing fine-grained data representati…
Matryoshka Re-Ranker: A Flexible Re-Ranking Architecture With Configurable Depth and Width
Zheng Liu, Chaofan Li, Shitao Xiao +3
Large language models (LLMs) provide powerful foundations to perform fine-grained text re-ranking. However, they are often prohibitive in reality due to constraints on computation…
Lighter And Better: Towards Flexible Context Adaptation For Retrieval Augmented Generation
Zheng Liu, Chenyuan Wu, Ninglu Shao +3
The existing Retrieval-Augmented Generation (RAG) systems face significant challenges in terms of cost and effectiveness. On one hand, they need to encode the lengthy retrieved con…