Don't Stop Pretraining? Make Prompt-based Fine-tuning Powerful Learner
暂无分享,去创建一个
[1] L. Ein-Dor,et al. Zero-Shot Text Classification with Self-Training , 2022, EMNLP.
[2] Zejiang Hou,et al. Meta-Learning the Difference: Preparing Large Language Models for Efficient Adaptation , 2022, Transactions of the Association for Computational Linguistics.
[3] Matthew E. Peters,et al. ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts , 2022, EMNLP.
[4] Yue Feng,et al. Learning to Execute Actions or Ask Clarification Questions , 2022, NAACL-HLT.
[5] Qiang Zhang,et al. StepGame: A New Benchmark for Robust Multi-Hop Spatial Reasoning in Texts , 2022, AAAI.
[6] Noah A. Smith,et al. Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks , 2022, EMNLP.
[7] Andrew M. Dai,et al. PaLM: Scaling Language Modeling with Pathways , 2022, J. Mach. Learn. Res..
[8] Lisa Anne Hendricks,et al. Training Compute-Optimal Large Language Models , 2022, ArXiv.
[9] Ryan J. Lowe,et al. Training language models to follow instructions with human feedback , 2022, NeurIPS.
[10] M. Lewis,et al. Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? , 2022, Conference on Empirical Methods in Natural Language Processing.
[11] Jianmin Wang,et al. Debiased Self-Training for Semi-Supervised Learning , 2022, NeurIPS.
[12] Shuohang Wang,et al. AdaPrompt: Adaptive Model Training for Prompt-based NLP , 2022, EMNLP.
[13] Jordan Massiah,et al. PARS: Pseudo-Label Aware Robust Sample Selection for Learning with Noisy Labels , 2022, ArXiv.
[14] Sanket Vaibhav Mehta,et al. ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning , 2021, ArXiv.
[15] Jie Zhou,et al. On Transferability of Prompt Tuning for Natural Language Processing , 2021, NAACL.
[16] M. Lewis,et al. MetaICL: Learning to Learn In Context , 2021, NAACL.
[17] Alexander M. Rush,et al. Multitask Prompted Training Enables Zero-Shot Task Generalization , 2021, ICLR.
[18] Brian Lester,et al. SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer , 2021, ACL.
[19] Minlie Huang,et al. PPT: Pre-trained Prompt Tuning for Few-shot Learning , 2021, ACL.
[20] Quoc V. Le,et al. Finetuned Language Models Are Zero-Shot Learners , 2021, ICLR.
[21] Fei Huang,et al. Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners , 2021, ICLR.
[22] Yelong Shen,et al. LoRA: Low-Rank Adaptation of Large Language Models , 2021, ICLR.
[23] Nicholas Carlini,et al. AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation , 2021, ICLR.
[24] Hannaneh Hajishirzi,et al. Cross-Task Generalization via Natural Language Crowdsourcing Instructions , 2021, ACL.
[25] Loïc Barrault,et al. On the Importance of Effectively Adapting Pretrained Language Models for Active Learning , 2021, ACL.
[26] T. Shinozaki,et al. FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling , 2021, NeurIPS.
[27] Xifeng Yan,et al. Task-adaptive Pre-training and Self-training are Complementary for Natural Language Understanding , 2021, EMNLP.
[28] Rong Jin,et al. Dash: Semi-Supervised Learning with Dynamic Thresholding , 2021, ICML.
[29] Charles Sutton,et al. Program Synthesis with Large Language Models , 2021, ArXiv.
[30] Brian Lester,et al. The Power of Scale for Parameter-Efficient Prompt Tuning , 2021, EMNLP.
[31] Guanghui Qin,et al. Learning How to Ask: Querying LMs with Mixtures of Soft Prompts , 2021, NAACL.
[32] Alexander M. Rush,et al. How many data points is a prompt worth? , 2021, NAACL.
[33] Emily M. Bender,et al. On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜 , 2021, FAccT.
[34] Mingsheng Long,et al. Self-Tuning for Data-Efficient Deep Learning , 2021, ICML.
[35] D. Klein,et al. Calibrate Before Use: Improving Few-Shot Performance of Language Models , 2021, ICML.
[36] Danqi Chen,et al. Making Pre-trained Language Models Better Few-shot Learners , 2021, ACL.
[37] Karen Hambardzumyan,et al. WARP: Word-level Adversarial ReProgramming , 2021, ACL.
[38] Colin Raffel,et al. Extracting Training Data from Large Language Models , 2020, USENIX Security Symposium.
[39] Colin Raffel,et al. mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer , 2020, NAACL.
[40] Hinrich Schütze,et al. It’s Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners , 2020, NAACL.
[41] Timo Schick,et al. Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference , 2020, EACL.
[42] Jihong Ouyang,et al. Semi-Supervised Text Classification with Balanced Deep Representation Distributions , 2021, ACL.
[43] Percy Liang,et al. Prefix-Tuning: Optimizing Continuous Prompts for Generation , 2021, ACL.
[44] Emily M. Bender,et al. Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data , 2020, ACL.
[45] Mark Chen,et al. Language Models are Few-Shot Learners , 2020, NeurIPS.
[46] Hannaneh Hajishirzi,et al. UnifiedQA: Crossing Format Boundaries With a Single QA System , 2020, FINDINGS.
[47] David Berthelot,et al. ReMixMatch: Semi-Supervised Learning with Distribution Matching and Augmentation Anchoring , 2020, ICLR.
[48] Diyi Yang,et al. MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification , 2020, ACL.
[49] Doug Downey,et al. Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks , 2020, ACL.
[50] Junnan Li,et al. DivideMix: Learning with Noisy Labels as Semi-supervised Learning , 2020, ICLR.
[51] Alec Radford,et al. Scaling Laws for Neural Language Models , 2020, ArXiv.
[52] David Berthelot,et al. FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence , 2020, NeurIPS.
[53] Quoc V. Le,et al. Self-Training With Noisy Student Improves ImageNet Classification , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[54] Colin Raffel,et al. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer , 2019, J. Mach. Learn. Res..
[55] Quoc V. Le,et al. Unsupervised Data Augmentation for Consistency Training , 2019, NeurIPS.
[56] Mirella Lapata,et al. Semi-Supervised Semantic Role Labeling with Cross-View Training , 2019, EMNLP.
[57] Gerard de Melo,et al. A Robust Self-Learning Framework for Cross-Lingual Text Classification , 2019, EMNLP.
[58] Omer Levy,et al. RoBERTa: A Robustly Optimized BERT Pretraining Approach , 2019, ArXiv.
[59] Ming-Wei Chang,et al. Zero-Shot Entity Linking by Reading Entity Descriptions , 2019, ACL.
[60] Noah A. Smith,et al. Variational Pretraining for Semi-supervised Text Classification , 2019, ACL.
[61] Xuanjing Huang,et al. How to Fine-Tune BERT for Text Classification? , 2019, CCL.
[62] Wei-Hung Weng,et al. Publicly Available Clinical BERT Embeddings , 2019, Proceedings of the 2nd Clinical Natural Language Processing Workshop.
[63] Myle Ott,et al. fairseq: A Fast, Extensible Toolkit for Sequence Modeling , 2019, NAACL.
[64] Iz Beltagy,et al. SciBERT: A Pretrained Language Model for Scientific Text , 2019, EMNLP.
[65] Samuel R. Bowman,et al. Neural Network Acceptability Judgments , 2018, Transactions of the Association for Computational Linguistics.
[66] Shin Ishii,et al. Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[67] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[68] Ilya Sutskever,et al. Language Models are Unsupervised Multitask Learners , 2019 .
[69] Eneko Agirre,et al. A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings , 2018, ACL.
[70] Omer Levy,et al. GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding , 2018, BlackboxNLP@EMNLP.
[71] Sebastian Ruder,et al. Universal Language Model Fine-tuning for Text Classification , 2018, ACL.
[72] Samuel R. Bowman,et al. A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference , 2017, NAACL.
[73] Eneko Agirre,et al. SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation , 2017, *SEMEVAL.
[74] Harri Valpola,et al. Weight-averaged consistency targets improve semi-supervised deep learning results , 2017, ArXiv.
[75] Timo Aila,et al. Temporal Ensembling for Semi-Supervised Learning , 2016, ICLR.
[76] Max Welling,et al. Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.
[77] Jian Zhang,et al. SQuAD: 100,000+ Questions for Machine Comprehension of Text , 2016, EMNLP.
[78] Xiang Zhang,et al. Character-level Convolutional Networks for Text Classification , 2015, NIPS.
[79] Christopher Potts,et al. A large annotated corpus for learning natural language inference , 2015, EMNLP.
[80] Jure Leskovec,et al. Hidden factors and hidden topics: understanding rating dimensions with review text , 2013, RecSys.
[81] Christopher Potts,et al. Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank , 2013, EMNLP.
[82] Dong-Hyun Lee,et al. Pseudo-Label : The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks , 2013 .
[83] Christopher Potts,et al. Learning Word Vectors for Sentiment Analysis , 2011, ACL.
[84] Jason Weston,et al. Curriculum learning , 2009, ICML '09.
[85] O. Chapelle,et al. Semi-Supervised Learning (Chapelle, O. et al., Eds.; 2006) [Book reviews] , 2009, IEEE Transactions on Neural Networks.
[86] Ido Dagan,et al. The Sixth PASCAL Recognizing Textual Entailment Challenge , 2009, TAC.
[87] Ming-Wei Chang,et al. Importance of Semantic Representation: Dataless Classification , 2008, AAAI.
[88] Eugene Charniak,et al. Effective Self-Training for Parsing , 2006, NAACL.
[89] Ido Dagan,et al. The Third PASCAL Recognizing Textual Entailment Challenge , 2007, ACL-PASCAL@ACL.
[90] Bo Pang,et al. Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales , 2005, ACL.
[91] Yoshua Bengio,et al. Semi-supervised Learning by Entropy Minimization , 2004, CAP.
[92] Claire Cardie,et al. Annotating Expressions of Opinions and Emotions in Language , 2005, Lang. Resour. Evaluation.
[93] Chris Brockett,et al. Automatically Constructing a Corpus of Sentential Paraphrases , 2005, IJCNLP.
[94] Bing Liu,et al. Mining and summarizing customer reviews , 2004, KDD.
[95] Bo Pang,et al. A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts , 2004, ACL.
[96] Ellen M. Voorhees,et al. Building a question answering test collection , 2000, SIGIR '00.
[97] David Yarowsky,et al. Unsupervised Word Sense Disambiguation Rivaling Supervised Methods , 1995, ACL.