Pengcheng He10 papers on this site (2019–2023)AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-TuningQingru Zhang, Minshuo Chen, Alexander Bukharin, et al.2023Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated FeedbackBaolin Peng, Michel Galley, Pengcheng He, et al.2023DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language ModelsYung-Sung Chuang, Yujia Xie, Hongyin Luo, et al.2023Instruction Tuning with GPT-4Baolin Peng, Chunyuan Li, Pengcheng He, et al.2023Query Rewriting in Retrieval-Augmented Large Language ModelsXinbei Ma, Yeyun Gong, Pengcheng He, et al.2023DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding SharingPengcheng He, Jianfeng Gao, Weizhu Chen2021DeBERTa: Decoding-enhanced BERT with Disentangled AttentionPengcheng He, Xiaodong Liu, Jianfeng Gao, et al.2020Generation-Augmented Retrieval for Open-domain Question AnsweringYuning Mao, Pengcheng He, Xiaodong Liu, et al.2020SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized OptimizationHaoming Jiang, Pengcheng He, Weizhu Chen, et al.2020Multi-Task Deep Neural Networks for Natural Language UnderstandingXiaodong Liu, Pengcheng He, Weizhu Chen, et al.2019