most citedImproving Contrastive Learning with Model Augmentation

12 citations · 17 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2022

Neural Retriever and Go Beyond: A Thesis Proposal

Man Luo

Information Retriever (IR) aims to find the relevant documents (e.g. snippets, passages, and articles) to a given query at large scale. IR plays an important role in many tasks suc…

cs.CL20222 cited

In-BoXBART: Get Instructions into Biomedical Multi-Task Learning

Mihir Parmar, Swaroop Mishra, Mirali Purohit +3

Single-task models have proven pivotal in solving specific tasks; however, they have limitations in real-world applications where multi-tasking is necessary and domain shifts are e…

cs.LG202212 cited

Improving Contrastive Learning with Model Augmentation

Zhiwei Liu, Yongjun Chen, Jia Li +3

The sequential recommendation aims at predicting the next items in user behaviors, which can be solved by characterizing item relationships in sequences. Due to the data sparsity a…

cs.CL20222 cited

Generalized but not Robust? Comparing the Effects of Data Modification Methods on Out-of-Domain Generalization and Adversarial Robustness

Tejas Gokhale, Swaroop Mishra, Man Luo +2

Data modification, either via additional training datasets, data augmentation, debiasing, and dataset filtering, has been proposed as an effective solution for generalizing to out-…

cs.CL20221 cited

Choose Your QA Model Wisely: A Systematic Study of Generative and Extractive Readers for Question Answering

Man Luo, Kazuma Hashimoto, Semih Yavuz +3

While both extractive and generative readers have been successfully applied to the Question Answering (QA) task, little attention has been paid toward the systematic comparison of…