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Knowledge probing is crucial for understanding the knowledge transfer mechanism behind the pre-trained language models (PLMs).
E-bert: Efficient-yet-effective entity embeddings for bert
Nina Poerner, Ulli Waltinger, and Hinrich Schütze. 2019 · 1911
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The unified medical language system (umls): integrating biomedical terminology
Olivier Bodenreider. 2004 · 2004
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Automatic evaluation of machine translation quality using longest common subsequence and skip-bigram statistics
Chin-Yew Lin and Franz Josef Och. 2004 · 2004
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Coder: Knowledge infused cross-lingual medical term embedding for term normalization
Zheng Yuan, Zhengyun Zhao, and Sheng Yu. 2020 · 2011
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Conceptnet 5.5: An open multilingual graph of general knowledge
Robyn Speer, Joshua Chin, and Catherine Havasi. 2017 · 2017
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018 · 2018
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Publicly available clinical bert embeddings
Emily Alsentzer, John Murphy, William Boag, Wei-Hung Weng, Di Jindi, Tristan Naumann, and Matthew McDermott. 2019 · 2019
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SciBERT: A pretrained language model for scientific text
Iz Beltagy, Kyle Lo, and Arman Cohan. 2019 · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Mask-predict: Parallel decoding of conditional masked language models
Marjan Ghazvininejad, Omer Levy, Yinhan Liu, and Luke Zettlemoyer. 2019 · 2019
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Yifan Peng, Shankai Yan, and Zhiyong Lu. 2019 · 2019
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Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander Miller. 2019 · 2019
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Language models are few-shot learners
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 2020
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Making pre-trained language models better few-shot learners
Tianyu Gao, Adam Fisch, and Danqi Chen. 2020 · 2020
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Domain-specific language model pretraining for biomedical natural language processing
Yu Gu, Robert Tinn, Hao Cheng, Michael Lucas, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, and Hoifung Poon. 2020 · 2020
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BioBERT: a pre-trained biomedical language representation model for biomedical text mining
Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang. 2020 · 2020
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Prompt tuning or fine-tuning-investigating relational knowledge in pre-trained language models
Leandra Fichtel, Jan-Christoph Kalo, and Wolf-Tilo Balke. 2021 · 2021
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Is supervised syntactic parsing beneficial for language understanding tasks? an empirical investigation
Goran Glavaš and Ivan Vulić. 2021 · 2021
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Multilingual lama: Investigating knowledge in multilingual pretrained language models
Nora Kassner, Philipp Dufter, and Hinrich Schütze. 2021 · 2021
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George Michalopoulos, Yuanxin Wang, Hussam Kaka, Helen Chen, and Alexander Wong. 2021 · 2021
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Scientific language models for biomedical knowledge base completion: An empirical study
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BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020 · 2020
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Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2020 · 2020
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E-bert: Efficient-yet-effective entity embeddings for bert
Nina Poerner, Ulli Waltinger, and Hinrich Schütze. 2020 · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 2020
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“you are grounded!”: Latent name artifacts in pre-trained language models
Vered Shwartz, Rachel Rudinger, and Oyvind Tafjord. 2020 · 2020
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Knowledgeable or educated guess? revisiting language models as knowledge bases
Boxi Cao, Hongyu Lin, Xianpei Han, Le Sun, Lingyong Yan, Meng Liao, Tong Xue, and Jin Xu. 2021 · 2021
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Autoregressive entity retrieval
Nicola De Cao, Gautier Izacard, Sebastian Riedel, and Fabio Petroni. 2021 · 2021
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Static embeddings as efficient knowledge bases?
Philipp Dufter, Nora Kassner, and Hinrich Schütze. 2021 · 2021
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Rahul Nadkarni, David Wadden, Iz Beltagy, Noah A Smith, Hannaneh Hajishirzi, and Tom Hope. 2021 · 2021
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KILT: a benchmark for knowledge intensive language tasks
Fabio Petroni, Aleksandra Piktus, Angela Fan, Patrick Lewis, Majid Yazdani, Nicola De Cao, James Thorne, Yacine Jernite, Vladimir Karpukhin, Jean Maillard, et al. 2021 · 2021
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Scifive: a text-to-text transformer model for biomedical literature
Long N Phan, James T Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, and Grégoire Altan-Bonnet. 2021 · 2021
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Learning how to ask: Querying lms with mixtures of soft prompts
Guanghui Qin and Jason Eisner. 2021 · 2021
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Tacl: Improving BERT pre-training with token-aware contrastive learning
Yixuan Su, Fangyu Liu, Zaiqiao Meng, Tian Lan, Lei Shu, Ehsan Shareghi, and Nigel Collier. 2021 · 2021
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Can language models be biomedical knowledge bases?
Mujeen Sung, Jinhyuk Lee, Sean Yi, Minji Jeon, Sungdong Kim, and Jaewoo Kang. 2021 · 2021
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Factual probing is [mask]: Learning vs. learning to recall
Zexuan Zhong, Dan Friedman, and Danqi Chen. 2021 · 2021
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A contrastive framework for neural text generation
Yixuan Su, Tian Lan, Yan Wang, Dani Yogatama, Lingpeng Kong, and Nigel Collier. 2022 · 2022
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