35 citations · 84 across the 7 of their papers we have counts for
7 papers
FinSQL: Model-Agnostic LLMs-based Text-to-SQL Framework for Financial Analysis
Chao Zhang, Yuren Mao, Yijiang Fan +5
Text-to-SQL, which provides zero-code interface for operating relational databases, has gained much attention in financial analysis; because, financial professionals may not well-s…
MultiEM: Efficient and Effective Unsupervised Multi-Table Entity Matching
Xiaocan Zeng, Pengfei Wang, Yuren Mao +3
Entity Matching (EM), which aims to identify all entity pairs referring to the same real-world entity from relational tables, is one of the most important tasks in real-world data…
C3: Zero-shot Text-to-SQL with ChatGPT
Xuemei Dong, Chao Zhang, Yuhang Ge +5
This paper proposes a ChatGPT-based zero-shot Text-to-SQL method, dubbed C3, which achieves 82.3\% in terms of execution accuracy on the holdout test set of Spider and becomes the…
Knowledge-refined Denoising Network for Robust Recommendation
Xinjun Zhu, Yuntao Du, Yuren Mao +3
Knowledge graph (KG), which contains rich side information, becomes an essential part to boost the recommendation performance and improve its explainability. However, existing know…
SparDL: Distributed Deep Learning Training with Efficient Sparse Communication
Minjun Zhao, Yichen Yin, Yuren Mao +3
Top-k sparsification has recently been widely used to reduce the communication volume in distributed deep learning. However, due to the Sparse Gradient Accumulation (SGA) dilemma,…
SoLar: Sinkhorn Label Refinery for Imbalanced Partial-Label Learning
Haobo Wang, Mingxuan Xia, Yixuan Li +4
Partial-label learning (PLL) is a peculiar weakly-supervised learning task where the training samples are generally associated with a set of candidate labels instead of single grou…
PromptEM: Prompt-tuning for Low-resource Generalized Entity Matching
Pengfei Wang, Xiaocan Zeng, Lu Chen +4
Entity Matching (EM), which aims to identify whether two entity records from two relational tables refer to the same real-world entity, is one of the fundamental problems in data m…