11 citations · 16 across the 3 of their papers we have counts for
3 papers
cs.CL2024★ 11 cited
Gecko: Versatile Text Embeddings Distilled from Large Language Models
Jinhyuk Lee, Zhuyun Dai, Xiaoqi Ren +17
We present Gecko, a compact and versatile text embedding model. Gecko achieves strong retrieval performance by leveraging a key idea: distilling knowledge from large language model…
cs.LG2023
SamToNe: Improving Contrastive Loss for Dual Encoder Retrieval Models with Same Tower Negatives
Fedor Moiseev, Gustavo Hernandez Abrego, Peter Dornbach +3
Dual encoders have been used for retrieval tasks and representation learning with good results. A standard way to train dual encoders is using a contrastive loss with in-batch nega…
cs.IR2021★ 5 cited
Large Dual Encoders Are Generalizable Retrievers
Jianmo Ni, Chen Qu, Jing Lu +8
It has been shown that dual encoders trained on one domain often fail to generalize to other domains for retrieval tasks. One widespread belief is that the bottleneck layer of a du…