9 citations · 16 across the 18 of their papers we have counts for
4 papers · 1 filter
Generating Synthetic Datasets for Few-shot Prompt Tuning
Xu Guo, Zilin Du, Boyang Li +1
A major limitation of prompt tuning is its dependence on large labeled training datasets. Under few-shot learning settings, prompt tuning lags far behind full-model fine-tuning, li…
RevMUX: Data Multiplexing with Reversible Adapters for Efficient LLM Batch Inference
Yige Xu, Xu Guo, Zhiwei Zeng +1
Large language models (LLMs) have brought a great breakthrough to the natural language processing (NLP) community, while leading the challenge of handling concurrent customer queri…
PairCFR: Enhancing Model Training on Paired Counterfactually Augmented Data through Contrastive Learning
Xiaoqi Qiu, Yongjie Wang, Xu Guo +4
Counterfactually Augmented Data (CAD) involves creating new data samples by applying minimal yet sufficient modifications to flip the label of existing data samples to other classe…
HGAttack: Transferable Heterogeneous Graph Adversarial Attack
He Zhao, Zhiwei Zeng, Yongwei Wang +2
Heterogeneous Graph Neural Networks (HGNNs) are increasingly recognized for their performance in areas like the web and e-commerce, where resilience against adversarial attacks is…