2 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.CL2024
StruEdit: Structured Outputs Enable the Fast and Accurate Knowledge Editing for Large Language Models
Baolong Bi, Shenghua Liu, Yiwei Wang +4
As the modern tool of choice for question answering, large language models (LLMs) are expected to deliver answers with up-to-date knowledge. To achieve such ideal question-answerin…
cs.CL2022★ 2 cited
Why Should Adversarial Perturbations be Imperceptible? Rethink the Research Paradigm in Adversarial NLP
Yangyi Chen, Hongcheng Gao, Ganqu Cui +4
Textual adversarial samples play important roles in multiple subfields of NLP research, including security, evaluation, explainability, and data augmentation. However, most work mi…
cs.CL2022★ 1 cited
Exploring the Universal Vulnerability of Prompt-based Learning Paradigm
Lei Xu, Yangyi Chen, Ganqu Cui +2
Prompt-based learning paradigm bridges the gap between pre-training and fine-tuning, and works effectively under the few-shot setting. However, we find that this learning paradigm…