402 citations · 1.1k across the 59 of their papers we have counts for
59 papers
Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation
Dawei Gao, Haibin Wang, Yaliang Li +4
Large language models (LLMs) have emerged as a new paradigm for Text-to-SQL task. However, the absence of a systematical benchmark inhibits the development of designing effective,…
TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series
Chenxi Sun, Hongyan Li, Yaliang Li +1
This work summarizes two ways to accomplish Time-Series (TS) tasks in today's Large Language Model (LLM) context: LLM-for-TS (model-centric) designs and trains a fundamental large…
Do Emergent Abilities Exist in Quantized Large Language Models: An Empirical Study
Peiyu Liu, Zikang Liu, Ze-Feng Gao +5
Despite the superior performance, Large Language Models~(LLMs) require significant computational resources for deployment and use. To overcome this issue, quantization methods have…
Efficient Personalized Federated Learning via Sparse Model-Adaptation
Daoyuan Chen, Liuyi Yao, Dawei Gao +2
Federated Learning (FL) aims to train machine learning models for multiple clients without sharing their own private data. Due to the heterogeneity of clients' local data distribut…
Counterfactual Debiasing for Generating Factually Consistent Text Summaries
Chenhe Dong, Yuexiang Xie, Yaliang Li +1
Despite substantial progress in abstractive text summarization to generate fluent and informative texts, the factual inconsistency in the generated summaries remains an important y…
Multi-grained Hypergraph Interest Modeling for Conversational Recommendation
Chenzhan Shang, Yupeng Hou, Wayne Xin Zhao +2
Conversational recommender system (CRS) interacts with users through multi-turn dialogues in natural language, which aims to provide high-quality recommendations for user's instant…
HPN: Personalized Federated Hyperparameter Optimization
Anda Cheng, Zhen Wang, Yaliang Li +1
Numerous research studies in the field of federated learning (FL) have attempted to use personalization to address the heterogeneity among clients, one of FL's most crucial and cha…
LON-GNN: Spectral GNNs with Learnable Orthonormal Basis
Qian Tao, Zhen Wang, Wenyuan Yu +2
In recent years, a plethora of spectral graph neural networks (GNN) methods have utilized polynomial basis with learnable coefficients to achieve top-tier performances on many node…
FS-Real: Towards Real-World Cross-Device Federated Learning
Daoyuan Chen, Dawei Gao, Yuexiang Xie +5
Federated Learning (FL) aims to train high-quality models in collaboration with distributed clients while not uploading their local data, which attracts increasing attention in bot…
Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks
Zeyu Qin, Liuyi Yao, Daoyuan Chen +3
In this work, besides improving prediction accuracy, we study whether personalization could bring robustness benefits to backdoor attacks. We conduct the first study of backdoor at…
Collaborating Heterogeneous Natural Language Processing Tasks via Federated Learning
Chenhe Dong, Yuexiang Xie, Bolin Ding +2
The increasing privacy concerns on personal private text data promote the development of federated learning (FL) in recent years. However, the existing studies on applying FL in NL…
Privacy-Preserved Neural Graph Similarity Learning
Yupeng Hou, Wayne Xin Zhao, Yaliang Li +1
To develop effective and efficient graph similarity learning (GSL) models, a series of data-driven neural algorithms have been proposed in recent years. Although GSL models are fre…
Fine-Grained Modeling and Optimization for Intelligent Resource Management in Big Data Processing
Chenghao Lyu, Qi Fan, Fei Song +8
Big data processing at the production scale presents a highly complex environment for resource optimization (RO), a problem crucial for meeting performance goals and budgetary cons…
Efficient End-to-End AutoML via Scalable Search Space Decomposition
Yang Li, Yu Shen, Wentao Zhang +2
End-to-end AutoML has attracted intensive interests from both academia and industry which automatically searches for ML pipelines in a space induced by feature engineering, algorit…
A Benchmark for Federated Hetero-Task Learning
Liuyi Yao, Dawei Gao, Zhen Wang +7
To investigate the heterogeneity in federated learning in real-world scenarios, we generalize the classic federated learning to federated hetero-task learning, which emphasizes the…
FedHPO-B: A Benchmark Suite for Federated Hyperparameter Optimization
Zhen Wang, Weirui Kuang, Ce Zhang +2
Hyperparameter optimization (HPO) is crucial for machine learning algorithms to achieve satisfactory performance, whose progress has been boosted by related benchmarks. Nonetheless…
Towards Universal Sequence Representation Learning for Recommender Systems
Yupeng Hou, Shanlei Mu, Wayne Xin Zhao +3
In order to develop effective sequential recommenders, a series of sequence representation learning (SRL) methods are proposed to model historical user behaviors. Most existing SRL…
ID-Agnostic User Behavior Pre-training for Sequential Recommendation
Shanlei Mu, Yupeng Hou, Wayne Xin Zhao +2
Recently, sequential recommendation has emerged as a widely studied topic. Existing researches mainly design effective neural architectures to model user behavior sequences based o…
pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning
Daoyuan Chen, Dawei Gao, Weirui Kuang +2
Personalized Federated Learning (pFL), which utilizes and deploys distinct local models, has gained increasing attention in recent years due to its success in handling the statisti…
EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks
Runlin Lei, Zhen Wang, Yaliang Li +2
Graph Neural Networks (GNNs) have received extensive research attention for their promising performance in graph machine learning. Despite their extraordinary predictive accuracy,…
FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learning
Zhen Wang, Weirui Kuang, Yuexiang Xie +4
The incredible development of federated learning (FL) has benefited various tasks in the domains of computer vision and natural language processing, and the existing frameworks suc…
FederatedScope: A Flexible Federated Learning Platform for Heterogeneity
Yuexiang Xie, Zhen Wang, Dawei Gao +6
Although remarkable progress has been made by existing federated learning (FL) platforms to provide infrastructures for development, these platforms may not well tackle the challen…
Towards Personalized Answer Generation in E-Commerce via Multi-Perspective Preference Modeling
Yang Deng, Yaliang Li, Wenxuan Zhang +2
Recently, Product Question Answering (PQA) on E-Commerce platforms has attracted increasing attention as it can act as an intelligent online shopping assistant and improve the cust…
HRKD: Hierarchical Relational Knowledge Distillation for Cross-domain Language Model Compression
Chenhe Dong, Yaliang Li, Ying Shen +1
On many natural language processing tasks, large pre-trained language models (PLMs) have shown overwhelming performances compared with traditional neural network methods. Neverthel…