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Prevalent supervised learning methods in natural language processing (NLP) are notoriously data-hungry, which demand large amounts of high-quality annotated data.
A sequential algorithm for training text classifiers
David D. Lewis and William A. Gale. 1994 · 1994
Earlier work this paper cites.
Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition
Erik F. Tjong Kim Sang and Fien De Meulder. 2003 · 2003
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Reducing labeling effort for structured prediction tasks
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Active learning literature survey
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Active learning by querying informative and representative examples
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Named entity recognition with bilingual constraints
Wanxiang Che, Mengqiu Wang, Christopher D. Manning, and Ting Liu. 2013 · 2013
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee. 2013 · 2013
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Position-aware attention and supervised data improve slot filling
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Learning to reweight examples for robust deep learning
Mengye Ren, Wenyuan Zeng, Bin Yang, and Raquel Urtasun. 2018 · 2018
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Active learning for convolutional neural networks: A core-set approach
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Matching the blanks: Distributional similarity for relation learning
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BERT: Pre-training of deep bidirectional transformers for language understanding
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Decoupled weight decay regularization
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Sentence-BERT: Sentence embeddings using Siamese BERT-networks
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Deep batch active learning by diverse, uncertain gradient lower bounds
Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, and Alekh Agarwal. 2020 · 2020
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Language models are few-shot learners
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Earlier work this paper cites.
Power-bert: Accelerating BERT inference via progressive word-vector elimination
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Differentially private representation for NLP: Formal guarantee and an empirical study on privacy and fairness
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Exploring the limits of transfer learning with a unified text-to-text transformer
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Making monolingual sentence embeddings multilingual using knowledge distillation
Nils Reimers and Iryna Gurevych. 2020 · 2020
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AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts
Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, and Sameer Singh. 2020 · 2020
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Cold-start active learning through self-supervised language modeling
Michelle Yuan, Hsuan-Tien Lin, and Jordan Boyd-Graber. 2020 · 2020
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Palm: Scaling language modeling with pathways
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OpenPrompt: An open-source framework for prompt-learning
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What makes good in-context examples for GPT-3?
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Summarization as indirect supervision for relation extraction
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Pre-training with whole word masking for chinese bert
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Language model as an annotator: Exploring DialoGPT for dialogue summarization
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Making pre-trained language models better few-shot learners
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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
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Active learning by acquiring contrastive examples
Katerina Margatina, Giorgos Vernikos, Loïc Barrault, and Nikolaos Aletras. 2021 · 2021
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Recent advances in natural language processing via large pre-trained language models: A survey
Bonan Min, Hayley Ross, Elior Sulem, Amir Pouran Ben Veyseh, Thien Huu Nguyen, Oscar Sainz, Eneko Agirre, Ilana Heinz, and Dan Roth. 2021 · 2021
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A survey of deep active learning
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Keming Lu, I-Hung Hsu, Wenxuan Zhou, Mingyu Derek Ma, and Muhao Chen. 2022a · 2022
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Generating training data with language models: Towards zero-shot language understanding
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Black-box tuning for language-model-as-a-service
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ZeroGen: Efficient zero-shot learning via dataset generation
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Differentially private fine-tuning of language models
Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin A. Inan, Gautam Kamath, Janardhan Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, Sergey Yekhanin, and Huishuai Zhang. 2022 · 2022
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Large language models as annotators: Enhancing generalization of nlp models at minimal cost
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Pumer: Pruning and merging tokens for efficient vision language models
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PLACES: Prompting language models for social conversation synthesis
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Annollm: Making large language models to be better crowdsourced annotators
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
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Why the pseudo label based semi-supervised learning algorithm is effective?
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Llama: Open and efficient foundation language models
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Gpt-ner: Named entity recognition via large language models
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