3 papers
cs.CL2025
When Retrieval Succeeds and Fails: Rethinking Retrieval-Augmented Generation for LLMs
Yongjie Wang, Yue Yu, Kaisong Song +2
Large Language Models (LLMs) have enabled a wide range of applications through their powerful capabilities in language understanding and generation. However, as LLMs are trained on…
cs.CL2024
A Survey on Natural Language Counterfactual Generation
Yongjie Wang, Xiaoqi Qiu, Yu Yue +4
Natural language counterfactual generation aims to minimally modify a given text such that the modified text will be classified into a different class. The generated counterfactual…
cs.LG2024
PairCFR: Enhancing Model Training on Paired Counterfactually Augmented Data through Contrastive Learning
Xiaoqi Qiu, Yongjie Wang, Xu Guo +4
Counterfactually Augmented Data (CAD) involves creating new data samples by applying minimal yet sufficient modifications to flip the label of existing data samples to other classe…