3 citations · 6 across the 8 of their papers we have counts for
8 papers
Stochastic Bridges as Effective Regularizers for Parameter-Efficient Tuning
Weize Chen, Xu Han, Yankai Lin +3
Parameter-efficient tuning methods (PETs) have achieved promising results in tuning large pre-trained language models (PLMs). By formalizing frozen PLMs and additional tunable para…
Plug-and-Play Document Modules for Pre-trained Models
Chaojun Xiao, Zhengyan Zhang, Xu Han +7
Large-scale pre-trained models (PTMs) have been widely used in document-oriented NLP tasks, such as question answering. However, the encoding-task coupling requirement results in t…
WebCPM: Interactive Web Search for Chinese Long-form Question Answering
Yujia Qin, Zihan Cai, Dian Jin +12
Long-form question answering (LFQA) aims at answering complex, open-ended questions with detailed, paragraph-length responses. The de facto paradigm of LFQA necessitates two proced…
Recyclable Tuning for Continual Pre-training
Yujia Qin, Cheng Qian, Xu Han +6
Continual pre-training is the paradigm where pre-trained language models (PLMs) continually acquire fresh knowledge from growing data and gradually get upgraded. Before an upgraded…
UNTER: A Unified Knowledge Interface for Enhancing Pre-trained Language Models
Deming Ye, Yankai Lin, Zhengyan Zhang +1
Recent research demonstrates that external knowledge injection can advance pre-trained language models (PLMs) in a variety of downstream NLP tasks. However, existing knowledge inje…
Manual-Guided Dialogue for Flexible Conversational Agents
Ryuichi Takanobu, Hao Zhou, Yankai Lin +3
How to build and use dialogue data efficiently, and how to deploy models in different domains at scale can be two critical issues in building a task-oriented dialogue system. In th…