14 citations · 42 across the 15 of their papers we have counts for
15 papers
UPFL: Unsupervised Personalized Federated Learning towards New Clients
Tiandi Ye, Cen Chen, Yinggui Wang +2
Personalized federated learning has gained significant attention as a promising approach to address the challenge of data heterogeneity. In this paper, we address a relatively unex…
You Can Backdoor Personalized Federated Learning
Tiandi Ye, Cen Chen, Yinggui Wang +2
Existing research primarily focuses on backdoor attacks and defenses within the generic federated learning scenario, where all clients collaborate to train a single global model. A…
TransCoder: Towards Unified Transferable Code Representation Learning Inspired by Human Skills
Qiushi Sun, Nuo Chen, Jianing Wang +2
Code pre-trained models (CodePTMs) have recently demonstrated a solid capacity to process various software intelligence tasks, e.g., code clone detection, code translation, and cod…
Boosting Language Models Reasoning with Chain-of-Knowledge Prompting
Jianing Wang, Qiushi Sun, Xiang Li +1
Recently, Chain-of-Thought (CoT) prompting has delivered success on complex reasoning tasks, which aims at designing a simple prompt like ``Let's think step by step'' or multiple i…
When Gradient Descent Meets Derivative-Free Optimization: A Match Made in Black-Box Scenario
Chengcheng Han, Liqing Cui, Renyu Zhu +5
Large pre-trained language models (PLMs) have garnered significant attention for their versatility and potential for solving a wide spectrum of natural language processing (NLP) ta…
Make Prompt-based Black-Box Tuning Colorful: Boosting Model Generalization from Three Orthogonal Perspectives
Qiushi Sun, Chengcheng Han, Nuo Chen +4
Large language models (LLMs) have shown increasing power on various natural language processing (NLP) tasks. However, tuning these models for downstream tasks usually needs exorbit…
GradMA: A Gradient-Memory-based Accelerated Federated Learning with Alleviated Catastrophic Forgetting
Kangyang Luo, Xiang Li, Yunshi Lan +1
Federated Learning (FL) has emerged as a de facto machine learning area and received rapid increasing research interests from the community. However, catastrophic forgetting caused…
HugNLP: A Unified and Comprehensive Library for Natural Language Processing
Jianing Wang, Nuo Chen, Qiushi Sun +3
In this paper, we introduce HugNLP, a unified and comprehensive library for natural language processing (NLP) with the prevalent backend of HuggingFace Transformers, which is desig…
Meta-Learning Triplet Network with Adaptive Margins for Few-Shot Named Entity Recognition
Chengcheng Han, Renyu Zhu, Jun Kuang +5
Meta-learning methods have been widely used in few-shot named entity recognition (NER), especially prototype-based methods. However, the Other(O) class is difficult to be represent…
SeeGera: Self-supervised Semi-implicit Graph Variational Auto-encoders with Masking
Xiang Li, Tiandi Ye, Caihua Shan +2
Generative graph self-supervised learning (SSL) aims to learn node representations by reconstructing the input graph data. However, most existing methods focus on unsupervised lear…
SpanProto: A Two-stage Span-based Prototypical Network for Few-shot Named Entity Recognition
Jianing Wang, Chengcheng Han, Chengyu Wang +5
Few-shot Named Entity Recognition (NER) aims to identify named entities with very little annotated data. Previous methods solve this problem based on token-wise classification, whi…
Knowledge Prompting in Pre-trained Language Model for Natural Language Understanding
Jianing Wang, Wenkang Huang, Qiuhui Shi +4
Knowledge-enhanced Pre-trained Language Model (PLM) has recently received significant attention, which aims to incorporate factual knowledge into PLMs. However, most existing metho…
CAT-probing: A Metric-based Approach to Interpret How Pre-trained Models for Programming Language Attend Code Structure
Nuo Chen, Qiushi Sun, Renyu Zhu +3
Code pre-trained models (CodePTMs) have recently demonstrated significant success in code intelligence. To interpret these models, some probing methods have been applied. However,…
Towards Unified Prompt Tuning for Few-shot Text Classification
Jianing Wang, Chengyu Wang, Fuli Luo +6
Prompt-based fine-tuning has boosted the performance of Pre-trained Language Models (PLMs) on few-shot text classification by employing task-specific prompts. Yet, PLMs are unfamil…
Cooperative Self-training of Machine Reading Comprehension
Hongyin Luo, Shang-Wen Li, Mingye Gao +2
Pretrained language models have significantly improved the performance of downstream language understanding tasks, including extractive question answering, by providing high-qualit…