156 citations · 185 across the 12 of their papers we have counts for
6 papers · 1 filter
RUEL: Retrieval-Augmented User Representation with Edge Browser Logs for Sequential Recommendation
Ning Wu, Ming Gong, Linjun Shou +2
Online recommender systems (RS) aim to match user needs with the vast amount of resources available on various platforms. A key challenge is to model user preferences accurately un…
Alleviating Over-smoothing for Unsupervised Sentence Representation
Nuo Chen, Linjun Shou, Ming Gong +5
Currently, learning better unsupervised sentence representations is the pursuit of many natural language processing communities. Lots of approaches based on pre-trained language mo…
Typos-aware Bottlenecked Pre-Training for Robust Dense Retrieval
Shengyao Zhuang, Linjun Shou, Jian Pei +4
Current dense retrievers (DRs) are limited in their ability to effectively process misspelled queries, which constitute a significant portion of query traffic in commercial search…
Lexicon-Enhanced Self-Supervised Training for Multilingual Dense Retrieval
Houxing Ren, Linjun Shou, Jian Pei +3
Recent multilingual pre-trained models have shown better performance in various multilingual tasks. However, these models perform poorly on multilingual retrieval tasks due to lack…
Bridge the Gap between Language models and Tabular Understanding
Nuo Chen, Linjun Shou, Ming Gong +5
Table pretrain-then-finetune paradigm has been proposed and employed at a rapid pace after the success of pre-training in the natural language domain. Despite the promising finding…
A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT
Ce Zhou, Qian Li, Chen Li +16
Pretrained Foundation Models (PFMs) are regarded as the foundation for various downstream tasks with different data modalities. A PFM (e.g., BERT, ChatGPT, and GPT-4) is trained on…