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In-context learning (ICL) has become the default method for using large language models (LLMs), making the exploration of its limitations and understanding the underlying causes crucial.
Benchmarking safe exploration in deep reinforcement learning
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Artificial intelligence, values, and alignment
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Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition
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ACE 2005 multilingual training corpus, 2005
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ACE (Automatic Content Extraction) English annotation guidelines for events
Linguistic Data Consortium · 2005
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ACE 2005 multilingual training corpus
Christopher Walker, Stephanie Strassel, Julie Medero, and Kazuaki Maeda · 2006
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Global inference for entity and relation identification via linear programming formulation, 2007
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Relation classification as two-way span-prediction
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SemEval-2010 task 8: Multi-way classification of semantic relations between pairs of nominals
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Recursive deep models for semantic compositionality over a sentiment treebank
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From light to Rich ERE: Annotation of entities, relations, and events
Zhiyi Song, Ann Bies, Stephanie Strassel, Tom Riese, Justin Mott, Joe Ellis, Jonathan Wright, Seth Kulick, Neville Ryant, and Xiaoyi Ma · 2015
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Position-aware attention and supervised data improve slot filling
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Scalable agent alignment via reward modeling: A research direction
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2018
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A multi-axis annotation scheme for event temporal relations
Qiang Ning, Hao Wu, and Dan Roth · 2018
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Sentence encoders on stilts: Supplementary training on intermediate labeled-data tasks
Jason Phang, Thibault Févry, and Samuel R Bowman · 2018
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FewRel 2.0: Towards more challenging few-shot relation classification
Tianyu Gao, Xu Han, Hao Zhu, Zhiyuan Liu, Peng Li, Maosong Sun, and Jie Zhou · 2019
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Parameter-efficient transfer learning for NLP
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly · 2019
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Story ending prediction by transferable BERT
Zhongyang Li, Xiao Ding, and Ting Liu · 2019
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DocRED: A large-scale document-level relation extraction dataset
Yuan Yao, Deming Ye, Peng Li, Xu Han, Yankai Lin, Zhenghao Liu, Zhiyuan Liu, Lixin Huang, Jie Zhou, and Maosong Sun · 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, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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GoEmotions: A dataset of fine-grained emotions
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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
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MAVEN: A massive general domain event detection dataset
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Transformers: State-of-the-Art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush · 2020
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Word-level textual adversarial attacking as combinatorial optimization
Yuan Zang, Fanchao Qi, Chenghao Yang, Zhiyuan Liu, Meng Zhang, Qun Liu, and Maosong Sun · 2020
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Few-NERD: A few-shot named entity recognition dataset
Ning Ding, Guangwei Xu, Yulin Chen, Xiaobin Wang, Xu Han, Pengjun Xie, Haitao Zheng, and Zhiyuan Liu · 2021
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Towards a unified view of parameter-efficient transfer learning
Junxian He, Chunting Zhou, Xuezhe Ma, Taylor Berg-Kirkpatrick, and Graham Neubig · 2021
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Measuring mathematical problem solving with the MATH dataset
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Entity-based knowledge conflicts in question answering
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A survey for in-context learning
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