887 citations · 1.2k across the 8 of their papers we have counts for
10 papers · 1 filter
IDPG: An Instance-Dependent Prompt Generation Method
Zhuofeng Wu, Sinong Wang, Jiatao Gu +4
Prompt tuning is a new, efficient NLP transfer learning paradigm that adds a task-specific prompt in each input instance during the model training stage. It freezes the pre-trained…
Entailment as Few-Shot Learner
Sinong Wang, Han Fang, Madian Khabsa +2
Large pre-trained language models (LMs) have demonstrated remarkable ability as few-shot learners. However, their success hinges largely on scaling model parameters to a degree tha…
On the Influence of Masking Policies in Intermediate Pre-training
Qinyuan Ye, Belinda Z. Li, Sinong Wang +5
Current NLP models are predominantly trained through a two-stage "pre-train then fine-tune" pipeline. Prior work has shown that inserting an intermediate pre-training stage, using…
Studying Strategically: Learning to Mask for Closed-book QA
Qinyuan Ye, Belinda Z. Li, Sinong Wang +5
Closed-book question-answering (QA) is a challenging task that requires a model to directly answer questions without access to external knowledge. It has been shown that directly f…
CLEAR: Contrastive Learning for Sentence Representation
Zhuofeng Wu, Sinong Wang, Jiatao Gu +3
Pre-trained language models have proven their unique powers in capturing implicit language features. However, most pre-training approaches focus on the word-level training objectiv…
Language Models as Fact Checkers?
Nayeon Lee, Belinda Z. Li, Sinong Wang +3
Recent work has suggested that language models (LMs) store both common-sense and factual knowledge learned from pre-training data. In this paper, we leverage this implicit knowledg…