most citedBGE Landmark Embedding: A Chunking-Free Embedding Method For Retrieval Augmented Long-Context Large Language Models

1 citations · 1 across the 3 of their papers we have counts for

collaborators
Showing cs.CLShow all

2 papers · 1 filter

cs.CL2024

Large Language Models as Foundations for Next-Gen Dense Retrieval: A Comprehensive Empirical Assessment

Kun Luo, Minghao Qin, Zheng Liu +3

Pretrained language models like BERT and T5 serve as crucial backbone encoders for dense retrieval. However, these models often exhibit limited generalization capabilities and face…

cs.CL2024★ 1 cited

BGE Landmark Embedding: A Chunking-Free Embedding Method For Retrieval Augmented Long-Context Large Language Models

Kun Luo, Zheng Liu, Shitao Xiao +1

Large language models (LLMs) call for extension of context to handle many critical applications. However, the existing approaches are prone to expensive costs and inferior quality…