Xi, Zhaohan, Du, Tianyu, Li, Changjiang, Pang, Ren, Ji, Shouling, Chen, Jinghui, Ma, Fenglong Ma, and Wang, Ting. Defending Pre-trained Language Models as Few-shot Learners against Backdoor Attacks. Retrieved from https://par.nsf.gov/biblio/10509527. Annual Conference on Neural Information Processing Systems (NeurIPS’23) .

Xi, Zhaohan, Du, Tianyu, Li, Changjiang, Pang, Ren, Ji, Shouling, Chen, Jinghui, Ma, Fenglong Ma, & Wang, Ting. Defending Pre-trained Language Models as Few-shot Learners against Backdoor Attacks. Annual Conference on Neural Information Processing Systems (NeurIPS’23), (). Retrieved from https://par.nsf.gov/biblio/10509527.

Xi, Zhaohan, Du, Tianyu, Li, Changjiang, Pang, Ren, Ji, Shouling, Chen, Jinghui, Ma, Fenglong Ma, and Wang, Ting. "Defending Pre-trained Language Models as Few-shot Learners against Backdoor Attacks". Annual Conference on Neural Information Processing Systems (NeurIPS’23) (). Country unknown/Code not available: Annual Conference on Neural Information Processing Systems. https://par.nsf.gov/biblio/10509527.

@article{osti_10509527,
place = {Country unknown/Code not available}, title = {Defending Pre-trained Language Models as Few-shot Learners against Backdoor Attacks}, url = {https://par.nsf.gov/biblio/10509527}, abstractNote = {}, journal = {Annual Conference on Neural Information Processing Systems (NeurIPS’23)}, publisher = {Annual Conference on Neural Information Processing Systems}, author = {Xi, Zhaohan and Du, Tianyu and Li, Changjiang and Pang, Ren and Ji, Shouling and Chen, Jinghui and Ma, Fenglong Ma and Wang, Ting}, }