123 citations · 278 across the 12 of their papers we have counts for
5 papers · 1 filter
LEAD: Liberal Feature-based Distillation for Dense Retrieval
Hao Sun, Xiao Liu, Yeyun Gong +6
Knowledge distillation is often used to transfer knowledge from a strong teacher model to a relatively weak student model. Traditional methods include response-based methods and fe…
Text Embeddings by Weakly-Supervised Contrastive Pre-training
Liang Wang, Nan Yang, Xiaolong Huang +5
This paper presents E5, a family of state-of-the-art text embeddings that transfer well to a wide range of tasks. The model is trained in a contrastive manner with weak supervision…
SimANS: Simple Ambiguous Negatives Sampling for Dense Text Retrieval
Kun Zhou, Yeyun Gong, Xiao Liu +8
Sampling proper negatives from a large document pool is vital to effectively train a dense retrieval model. However, existing negative sampling strategies suffer from the uninforma…
PROD: Progressive Distillation for Dense Retrieval
Zhenghao Lin, Yeyun Gong, Xiao Liu +8
Knowledge distillation is an effective way to transfer knowledge from a strong teacher to an efficient student model. Ideally, we expect the better the teacher is, the better the s…
SimLM: Pre-training with Representation Bottleneck for Dense Passage Retrieval
Liang Wang, Nan Yang, Xiaolong Huang +5
In this paper, we propose SimLM (Similarity matching with Language Model pre-training), a simple yet effective pre-training method for dense passage retrieval. It employs a simple…