84 citations · 681 across the 95 of their papers we have counts for
6 papers · 1 filter
Skim and Skip: Hierarchical Adaptive Inference for Efficient Multimodal Retrieval
Meng Gao, Yizhen Zhang, Yang Ding +10
Universal multimodal retrieval (UMR) increasingly adopts multimodal large language models (MLLMs) as unified embedding backbones, but their strong retrieval performance comes at su…
MS MARCO Web Search: a Large-scale Information-rich Web Dataset with Millions of Real Click Labels
Qi Chen, Xiubo Geng, Corby Rosset +28
Recent breakthroughs in large models have highlighted the critical significance of data scale, labels and modals. In this paper, we introduce MS MARCO Web Search, the first large-s…
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…
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…
Distill-VQ: Learning Retrieval Oriented Vector Quantization By Distilling Knowledge from Dense Embeddings
Shitao Xiao, Zheng Liu, Weihao Han +10
Vector quantization (VQ) based ANN indexes, such as Inverted File System (IVF) and Product Quantization (PQ), have been widely applied to embedding based document retrieval thanks…
ProphetNet-Ads: A Looking Ahead Strategy for Generative Retrieval Models in Sponsored Search Engine
Weizhen Qi, Yeyun Gong, Yu Yan +6
In a sponsored search engine, generative retrieval models are recently proposed to mine relevant advertisement keywords for users' input queries. Generative retrieval models genera…