21 citations · 59 across the 10 of their papers we have counts for
15 papers
Efficiently Tuned Parameters are Task Embeddings
Wangchunshu Zhou, Canwen Xu, Julian McAuley
Intermediate-task transfer can benefit a wide range of NLP tasks with properly selected source datasets. However, it is computationally infeasible to experiment with all intermedia…
LaPraDoR: Unsupervised Pretrained Dense Retriever for Zero-Shot Text Retrieval
Canwen Xu, Daya Guo, Nan Duan +1
In this paper, we propose LaPraDoR, a pretrained dual-tower dense retriever that does not require any supervised data for training. Specifically, we first present Iterative Contras…
Automatic Multi-Label Prompting: Simple and Interpretable Few-Shot Classification
Han Wang, Canwen Xu, Julian McAuley
Prompt-based learning (i.e., prompting) is an emerging paradigm for exploiting knowledge learned by a pretrained language model. In this paper, we propose Automatic Multi-Label Pro…
PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts
Stephen H. Bach, Victor Sanh, Zheng-Xin Yong +24
PromptSource is a system for creating, sharing, and using natural language prompts. Prompts are functions that map an example from a dataset to a natural language input and target…
Leashing the Inner Demons: Self-Detoxification for Language Models
Canwen Xu, Zexue He, Zhankui He +1
Language models (LMs) can reproduce (or amplify) toxic language seen during training, which poses a risk to their practical application. In this paper, we conduct extensive experim…
Beyond Preserved Accuracy: Evaluating Loyalty and Robustness of BERT Compression
Canwen Xu, Wangchunshu Zhou, Tao Ge +3
Recent studies on compression of pretrained language models (e.g., BERT) usually use preserved accuracy as the metric for evaluation. In this paper, we propose two new metrics, lab…