Search Results for author:
Found 11 papers, 4 papers with code
MetaWeighting: Learning to Weight Tasks in Multi-Task Learning
Task weighting, which assigns weights on the including tasks during training, significantly matters the performance of Multi-task Learning (MTL); thus, recently, there has been an explosive interest in it.
Adaptive Adversarial Multi-task Representation Learning
Adversarial Multi-task Representation Learning (AMTRL) methods are able to boost the performance of Multi-task Representation Learning (MTRL) models.
FIT-RAG: Black-Box RAG with Factual Information and Token Reduction
Simply concatenating all the retrieved documents brings large amounts of unnecessary tokens for LLMs, which degenerates the efficiency of black-box RAG.
FinSQL: Model-Agnostic LLMs-based Text-to-SQL Framework for Financial Analysis
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-skilled in SQL programming.
MultiEM: Efficient and Effective Unsupervised Multi-Table Entity Matching
Traditional unsupervised EM assumes that all entities come from two tables; however, it is more common to match entities from multiple tables in practical applications, that is, multi-table entity matching (multi-table EM).
C3: Zero-shot Text-to-SQL with ChatGPT
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 state-of-the-art zero-shot Text-to-SQL method on the Spider Challenge.
Ranked #4 on
Text-To-SQL
on spider
Knowledge-refined Denoising Network for Robust Recommendation
Knowledge graph (KG), which contains rich side information, becomes an essential part to boost the recommendation performance and improve its explainability.
SparDL: Distributed Deep Learning Training with Efficient Sparse Communication
Recently, a few methods have been put forward to handle the SGA dilemma.
SoLar: Sinkhorn Label Refinery for Imbalanced Partial-Label Learning
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 ground truth.
BanditMTL: Bandit-based Multi-task Learning for Text Classification
Task variance regularization, which can be used to improve the generalization of Multi-task Learning (MTL) models, remains unexplored in multi-task text classification.
Tchebycheff Procedure for Multi-task Text Classification
Multi-task Learning methods have achieved great progress in text classification.
