How many data points is a prompt worth?
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[1] Nanyun Peng,et al. The Woman Worked as a Babysitter: On Biases in Language Generation , 2019, EMNLP.
[2] Dan Roth,et al. Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences , 2018, NAACL.
[3] Marius Mosbach,et al. On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines , 2020, ArXiv.
[4] Kyunghyun Cho,et al. Mixout: Effective Regularization to Finetune Large-scale Pretrained Language Models , 2020, ICLR.
[5] Xiaodong Liu,et al. Unified Language Model Pre-training for Natural Language Understanding and Generation , 2019, NeurIPS.
[6] Omer Levy,et al. SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems , 2019, NeurIPS.
[7] Ido Dagan,et al. The Third PASCAL Recognizing Textual Entailment Challenge , 2007, ACL-PASCAL@ACL.
[8] Colin Raffel,et al. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer , 2019, J. Mach. Learn. Res..
[9] Zornitsa Kozareva,et al. SemEval-2012 Task 7: Choice of Plausible Alternatives: An Evaluation of Commonsense Causal Reasoning , 2011, *SEMEVAL.
[10] Judith Tonhauser,et al. The CommitmentBank: Investigating projection in naturally occurring discourse , 2019 .
[11] Helmut Schmid,et al. Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification , 2020, COLING.
[12] R. Thomas McCoy,et al. BERTs of a feather do not generalize together: Large variability in generalization across models with similar test set performance , 2020, BLACKBOXNLP.
[13] Kilian Q. Weinberger,et al. Revisiting Few-sample BERT Fine-tuning , 2020, ArXiv.
[14] Ali Farhadi,et al. Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping , 2020, ArXiv.
[15] José Camacho-Collados,et al. WiC: 10, 000 Example Pairs for Evaluating Context-Sensitive Representations , 2018, NAACL 2019.
[16] Ming-Wei Chang,et al. BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions , 2019, NAACL.
[17] Timo Schick,et al. Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference , 2020, EACL.
[18] Hinrich Schutze,et al. It’s Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners , 2020, NAACL.
[19] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[20] Quoc V. Le,et al. A Simple Method for Commonsense Reasoning , 2018, ArXiv.
[21] Xiaodong Liu,et al. ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension , 2018, ArXiv.
[22] Alec Radford,et al. Improving Language Understanding by Generative Pre-Training , 2018 .
[23] Thomas Wolf,et al. HuggingFace's Transformers: State-of-the-art Natural Language Processing , 2019, ArXiv.
[24] Kevin Gimpel,et al. ALBERT: A Lite BERT for Self-supervised Learning of Language Representations , 2019, ICLR.
[25] Samuel R. Bowman,et al. A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference , 2017, NAACL.
[26] Omer Levy,et al. RoBERTa: A Robustly Optimized BERT Pretraining Approach , 2019, ArXiv.
[27] Graham Neubig,et al. How Can We Know What Language Models Know? , 2019, Transactions of the Association for Computational Linguistics.
[28] Richard Socher,et al. The Natural Language Decathlon: Multitask Learning as Question Answering , 2018, ArXiv.
[29] Bryan Catanzaro,et al. Zero-shot Text Classification With Generative Language Models , 2019, ArXiv.
[30] Hector J. Levesque,et al. The Winograd Schema Challenge , 2011, AAAI Spring Symposium: Logical Formalizations of Commonsense Reasoning.
[31] Ilya Sutskever,et al. Language Models are Unsupervised Multitask Learners , 2019 .