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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…