623 citations · 2.1k across the 79 of their papers we have counts for
16 papers · 1 filter
Uncovering inequalities in new knowledge learning by large language models across different languages
Chenglong Wang, Haoyu Tang, Xiyuan Yang +8
As large language models (LLMs) gradually become integral tools for problem solving in daily life worldwide, understanding linguistic inequality is becoming increasingly important.…
Benchmarking and Defending Against Indirect Prompt Injection Attacks on Large Language Models
Jingwei Yi, Yueqi Xie, Bin Zhu +4
The integration of large language models with external content has enabled applications such as Microsoft Copilot but also introduced vulnerabilities to indirect prompt injection a…
Are You Copying My Model? Protecting the Copyright of Large Language Models for EaaS via Backdoor Watermark
Wenjun Peng, Jingwei Yi, Fangzhao Wu +7
Large language models (LLMs) have demonstrated powerful capabilities in both text understanding and generation. Companies have begun to offer Embedding as a Service (EaaS) based on…
NoisyTune: A Little Noise Can Help You Finetune Pretrained Language Models Better
Chuhan Wu, Fangzhao Wu, Tao Qi +2
Effectively finetuning pretrained language models (PLMs) is critical for their success in downstream tasks. However, PLMs may have risks in overfitting the pretraining tasks and da…
Fastformer: Additive Attention Can Be All You Need
Chuhan Wu, Fangzhao Wu, Tao Qi +2
Transformer is a powerful model for text understanding. However, it is inefficient due to its quadratic complexity to input sequence length. Although there are many methods on Tran…
Smart Bird: Learnable Sparse Attention for Efficient and Effective Transformer
Chuhan Wu, Fangzhao Wu, Tao Qi +4
Transformer has achieved great success in NLP. However, the quadratic complexity of the self-attention mechanism in Transformer makes it inefficient in handling long sequences. Man…
One Teacher is Enough? Pre-trained Language Model Distillation from Multiple Teachers
Chuhan Wu, Fangzhao Wu, Yongfeng Huang
Pre-trained language models (PLMs) achieve great success in NLP. However, their huge model sizes hinder their applications in many practical systems. Knowledge distillation is a po…
Hi-Transformer: Hierarchical Interactive Transformer for Efficient and Effective Long Document Modeling
Chuhan Wu, Fangzhao Wu, Tao Qi +1
Transformer is important for text modeling. However, it has difficulty in handling long documents due to the quadratic complexity with input text length. In order to handle this pr…
NewsBERT: Distilling Pre-trained Language Model for Intelligent News Application
Chuhan Wu, Fangzhao Wu, Yang Yu +3
Pre-trained language models (PLMs) like BERT have made great progress in NLP. News articles usually contain rich textual information, and PLMs have the potentials to enhance news t…
Improving Attention Mechanism with Query-Value Interaction
Chuhan Wu, Fangzhao Wu, Tao Qi +1
Attention mechanism has played critical roles in various state-of-the-art NLP models such as Transformer and BERT. It can be formulated as a ternary function that maps the input qu…
DA-Transformer: Distance-aware Transformer
Chuhan Wu, Fangzhao Wu, Yongfeng Huang
Transformer has achieved great success in the NLP field by composing various advanced models like BERT and GPT. However, Transformer and its existing variants may not be optimal in…
FedNER: Privacy-preserving Medical Named Entity Recognition with Federated Learning
Suyu Ge, Fangzhao Wu, Chuhan Wu +3
Medical named entity recognition (NER) has wide applications in intelligent healthcare. Sufficient labeled data is critical for training accurate medical NER model. However, the la…
Neural News Recommendation with Attentive Multi-View Learning
Chuhan Wu, Fangzhao Wu, Mingxiao An +3
Personalized news recommendation is very important for online news platforms to help users find interested news and improve user experience. News and user representation learning i…
Neural Chinese Named Entity Recognition via CNN-LSTM-CRF and Joint Training with Word Segmentation
Fangzhao Wu, Junxin Liu, Chuhan Wu +2
Chinese named entity recognition (CNER) is an important task in Chinese natural language processing field. However, CNER is very challenging since Chinese entity names are highly c…
Neural Chinese Word Segmentation with Lexicon and Unlabeled Data via Posterior Regularization
Junxin Liu, Fangzhao Wu, Chuhan Wu +2
Existing methods for CWS usually rely on a large number of labeled sentences to train word segmentation models, which are expensive and time-consuming to annotate. Luckily, the unl…
Neural Chinese Word Segmentation with Dictionary Knowledge
Junxin Liu, Fangzhao Wu, Chuhan Wu +2
Chinese word segmentation (CWS) is an important task for Chinese NLP. Recently, many neural network based methods have been proposed for CWS. However, these methods require a large…