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BERTRAM: Improved Word Embeddings Have Big Impact on Contextualized Model Performance., and . CoRR, (2019)Rare Words: A Major Problem for Contextualized Embeddings And How to Fix it by Attentive Mimicking., and . CoRR, (2019)Exploiting Cloze Questions for Few-Shot Text Classification and Natural Language Inference., and . CoRR, (2020)Atlas: Few-shot Learning with Retrieval Augmented Language Models, , , , , , , , , and . (2022)cite arxiv:2208.03299.LongForm: Optimizing Instruction Tuning for Long Text Generation with Corpus Extraction., , , and . CoRR, (2023)Augmented Language Models: a Survey., , , , , , , , , and 3 other author(s). CoRR, (2023)Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP., , and . CoRR, (2021)EditEval: An Instruction-Based Benchmark for Text Improvements., , , , , , , , and . CoRR, (2022)Rare Words: A Major Problem for Contextualized Embeddings and How to Fix it by Attentive Mimicking., and . AAAI, page 8766-8774. AAAI Press, (2020)Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification., , and . COLING, page 5569-5578. International Committee on Computational Linguistics, (2020)