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Task semantics can be expressed by a set of input-output examples or a piece of textual instruction.
Massively multilingual neural machine translation in the wild: Findings and challenges
Naveen Arivazhagan, Ankur Bapna, Orhan Firat, Dmitry Lepikhin, Melvin Johnson, Maxim Krikun, Mia Xu Chen, Yuan Cao, George F. Foster, Colin Cherry, Wolfgang Macherey, Zhifeng Chen, and Yonghui Wu. 2019 · 1907
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UNIFIEDQA: Crossing format boundaries with a single QA system
Daniel Khashabi, Sewon Min, Tushar Khot, Ashish Sabharwal, Oyvind Tafjord, Peter Clark, and Hannaneh Hajishirzi. 2020 · 1907
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HuggingFace’s Transformers: State-of-the-Art Natural Language Processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al. 2019 · 1910
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Instruction induction: From few examples to natural language task descriptions
Or Honovich, Uri Shaham, Samuel R. Bowman, and Omer Levy. 2023b · 1952
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Pre-learning environment representations for data-efficient neural instruction following
David Gaddy and Dan Klein. 2019 · 1956
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A markovian decision process
Richard Bellman. 1957 · 1957
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Cognitively guided instruction: A knowledge base for reform in primary mathematics instruction
Thomas P Carpenter, Elizabeth Fennema, and Megan L Franke. 1996 · 1996
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A longitudinal study of learning to use children’s thinking in mathematics instruction
Elizabeth Fennema, Thomas P Carpenter, Megan L Franke, Linda Levi, Victoria R Jacobs, and Susan B Empson. 1996 · 1996
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Long short-term memory
Sepp Hochreiter and Jurgen Schmidhuber. 1997 · 1997
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. 1998 · 1998
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Guiding a reinforcement learner with natural language advice: Initial results in robocup soccer
Gregory Kuhlmann, Peter Stone, Raymond Mooney, and Jude Shavlik. 2004 · 2004
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ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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Introduction: Aspects of Artificial General Intelligence
Pei Wang and Ben Goertzel. 2007 · 2006
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Learning to sportscast: a test of grounded language acquisition
David L. Chen and Raymond J. Mooney. 2008 · 2008
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Reading to learn: Constructing features from semantic abstracts
Jacob Eisenstein, James Clarke, Dan Goldwasser, and Dan Roth. 2009 · 2009
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Learning semantic correspondences with less supervision
Percy Liang, Michael Jordan, and Dan Klein. 2009 · 2009
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Reading between the lines: Learning to map high-level instructions to commands
S.R.K. Branavan, Luke Zettlemoyer, and Regina Barzilay. 2010 · 2010
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Driving semantic parsing from the world’s response
James Clarke, Dan Goldwasser, Ming-Wei Chang, and Dan Roth. 2010 · 2010
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The turking test: Can language models understand instructions?
Avia Efrat and Omer Levy. 2020 · 2010
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Learning to follow navigational directions
Adam Vogel and Daniel Jurafsky. 2010 · 2010
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Learning to win by reading manuals in a Monte-Carlo framework
S.R.K. Branavan, David Silver, and Regina Barzilay. 2011 · 2011
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Learning to interpret natural language navigation instructions from observations
David L. Chen and Raymond J. Mooney. 2011 · 2011
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Learning from natural instructions
Dan Goldwasser and Dan Roth. 2011 · 2011
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Approaching the symbol grounding problem with probabilistic graphical models
Stefanie Tellex, Thomas Kollar, Steven Dickerson, Matthew R Walter, Ashis Gopal Banerjee, Seth Teller, and Nicholas Roy. 2011 · 2011
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Learning to interpret natural language instructions
Monica Babeş-Vroman, James MacGlashan, Ruoyuan Gao, Kevin Winner, Richard Adjogah, Marie desJardins, Michael Littman, and Smaranda Muresan. 2012 · 2012
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Fast online lexicon learning for grounded language acquisition
David Chen. 2012 · 2012
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Unsupervised PCFG induction for grounded language learning with highly ambiguous supervision
Joohyun Kim and Raymond Mooney. 2012 · 2012
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A joint model of language and perception for grounded attribute learning
Cynthia Matuszek, Nicholas FitzGerald, Luke S. Zettlemoyer, Liefeng Bo, and Dieter Fox. 2012 · 2012
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Weakly supervised learning of semantic parsers for mapping instructions to actions
Yoav Artzi and Luke Zettlemoyer. 2013 · 2013
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Jointly learning to parse and perceive: Connecting natural language to the physical world
Jayant Krishnamurthy and Thomas Kollar. 2013 · 2013
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Artificial General Intelligence: Concept, State of The Art, and Future Prospects
Ben Goertzel. 2014 · 2014
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Natural language communication with robots
Yonatan Bisk, Deniz Yuret, and Daniel Marcu. 2016 · 2016
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SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Hypernetworks
David Ha, Andrew M. Dai, and Quoc V. Le. 2017 · 2017
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Joint concept learning and semantic parsing from natural language explanations
Shashank Srivastava, Igor Labutov, and Tom Mitchell. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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SMASH: one-shot model architecture search through hypernetworks
Andrew Brock, Theodore Lim, James M. Ritchie, and Nick Weston. 2018 · 2018
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How does “not left” become “right”? electrophysiological evidence for a dynamic conflict-bound negation processing account
Carolin Dudschig and Barbara Kaup. 2018 · 2018
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Training classifiers with natural language explanations
Braden Hancock, Paroma Varma, Stephanie Wang, Martin Bringmann, Percy Liang, and Christopher Ré. 2018 · 2018
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Constituency parsing with a self-attentive encoder
Nikita Kitaev and Dan Klein. 2018 · 2018
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Stress test evaluation for natural language inference
Aakanksha Naik, Abhilasha Ravichander, Norman Sadeh, Carolyn Rose, and Graham Neubig. 2018 · 2018
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Virtualhome: Simulating household activities via programs
Xavier Puig, Kevin Ra, Marko Boben, Jiaman Li, Tingwu Wang, Sanja Fidler, and Antonio Torralba. 2018 · 2018
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Zero-shot learning of classifiers from natural language quantification
Shashank Srivastava, Igor Labutov, and Tom Mitchell. 2018 · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019a · 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. 2019b · 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 · 2019
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Q-learning algorithms: A comprehensive classification and applications
Beakcheol Jang, Myeonghwi Kim, Gaspard Harerimana, and Jong Wook Kim. 2019 · 2019
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Language Models are Unsupervised Multitask Learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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Energy and policy considerations for deep learning in NLP
Emma Strubell, Ananya Ganesh, and Andrew McCallum. 2019 · 2019
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Benchmarking zero-shot text classification: Datasets, evaluation and entailment approach
Wenpeng Yin, Jamaal Hay, and Dan Roth. 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, 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 · 2020
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Language to network: Conditional parameter adaptation with natural language descriptions
Tian Jin, Zhun Liu, Shengjia Yan, Alexandre Eichenberger, and Louis-Philippe Morency. 2020 · 2020
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Negated and misprimed probes for pretrained language models: Birds can talk, but cannot fly
Nora Kassner and Hinrich Schütze. 2020 · 2020
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Interactive task learning from GUI-grounded natural language instructions and demonstrations
Toby Jia-Jun Li, Tom Mitchell, and Brad Myers. 2020 · 2020
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ExpBERT: Representation engineering with natural language explanations
Shikhar Murty, Pang Wei Koh, and Percy Liang. 2020 · 2020
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Massively multilingual ASR: 50 languages, 1 model, 1 billion parameters
Vineel Pratap, Anuroop Sriram, Paden Tomasello, Awni Hannun, Vitaliy Liptchinsky, Gabriel Synnaeve, and Ronan Collobert. 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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Learning to summarize with human feedback
Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel M. Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul F. Christiano. 2020 · 2020
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Learning from explanations with neural execution tree
Ziqi Wang, Yujia Qin, Wenxuan Zhou, Jun Yan, Qinyuan Ye, Leonardo Neves, Zhiyuan Liu, and Xiang Ren. 2020 · 2020
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Learning from task descriptions
Orion Weller, Nicholas Lourie, Matt Gardner, and Matthew E. Peters. 2020 · 2020
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Teaching machine comprehension with compositional explanations
Qinyuan Ye, Xiao Huang, Elizabeth Boschee, and Xiang Ren. 2020 · 2020
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Analogous process structure induction for sub-event sequence prediction
Hongming Zhang, Muhao Chen, Haoyu Wang, Yangqiu Song, and Dan Roth. 2020 · 2020
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Template-based named entity recognition using BART
Leyang Cui, Yu Wu, Jian Liu, Sen Yang, and Yue Zhang. 2021 · 2021
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Making pre-trained language models better few-shot learners
Tianyu Gao, Adam Fisch, and Danqi Chen. 2021 · 2021
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Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2021 · 2021
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Understanding by understanding not: Modeling negation in language models
Arian Hosseini, Siva Reddy, Dzmitry Bahdanau, R Devon Hjelm, Alessandro Sordoni, and Aaron Courville. 2021 · 2021
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A Survey of NLP-related Crowdsourcing Hits: What Works and What Does Not
Jessica Huynh, Jeffrey Bigham, and Maxine Eskenazi. 2021 · 2021
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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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Hello dolly: Democratizing the magic of chatgpt with open models
Mike Conover, Matt Hayes, Matt Mathur, Xiangrui Meng, Jianwei Xie, Jun Wan, Ali Ghodsi, Patrick Wendell, and Patrick Zaharia. 2023 · 2023
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Mind2web: Towards a generalist agent for the web
Xiang Deng, Yu Gu, Boyuan Zheng, Shijie Chen, Samual Stevens, Boshi Wang, Huan Sun, and Yu Su. 2023 · 2023
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Enhancing chat language models by scaling high-quality instructional conversations
Ning Ding, Yulin Chen, Bokai Xu, Yujia Qin, Shengding Hu, Zhiyuan Liu, Maosong Sun, and Bowen Zhou. 2023 · 2023
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A Survey on In-Context Learning
Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu Sun, Jingjing Xu, et al. 2023 · 2023
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Alpacafarm: A simulation framework for methods that learn from human feedback
Yann Dubois, Chen Xuechen Li, Rohan Taori, Tianyi Zhang, Ishaan Gulrajani, Jimmy Ba, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023 · 2023
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Xiang Lisa Li and Percy Liang. 2021 · 2021
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Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, and Jie Tang. 2021 · 2021
Cited alongside, same era.
Learning how to ask: Querying LMs with mixtures of soft prompts
Guanghui Qin and Jason Eisner. 2021 · 2021
Cited alongside, same era.
Label verbalization and entailment for effective zero and few-shot relation extraction
Oscar Sainz, Oier Lopez de Lacalle, Gorka Labaka, Ander Barrena, and Eneko Agirre. 2021 · 2021
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Few-shot text generation with natural language instructions
Timo Schick and Hinrich Schütze. 2021b · 2021
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It’s not just size that matters: Small language models are also few-shot learners
Timo Schick and Hinrich Schütze. 2021c · 2021
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Incremental few-shot text classification with multi-round new classes: Formulation, dataset and system
Congying Xia, Wenpeng Yin, Yihao Feng, and Philip Yu. 2021 · 2021
Cited alongside, same era.
The devil is in the errors: Leveraging large language models for fine-grained machine translation evaluation
Patrick Fernandes, Daniel Deutsch, Mara Finkelstein, Parker Riley, André FT Martins, Graham Neubig, Ankush Garg, Jonathan H Clark, Markus Freitag, and Orhan Firat. 2023 · 2023
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Koala: A dialogue model for academic research
Xinyang Geng, Arnav Gudibande, Hao Liu, Eric Wallace, Pieter Abbeel, Sergey Levine, and Dawn Song. 2023 · 2023
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Demystifying prompts in language models via perplexity estimation
Hila Gonen, Srini Iyer, Terra Blevins, Noah A. Smith, and Luke Zettlemoyer. 2023 · 2023
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Robustness of learning from task instructions
Jiasheng Gu, Hongyu Zhao, Hanzi Xu, Liangyu Nie, Hongyuan Mei, and Wenpeng Yin. 2023 · 2023
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Instruction tuned models are quick learners
Himanshu Gupta, Saurabh Arjun Sawant, Swaroop Mishra, Mutsumi Nakamura, Arindam Mitra, Santosh Mashetty, and Chitta Baral. 2023 · 2023
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Annollm: Making large language models to be better crowdsourced annotators
Xingwei He, Zhenghao Lin, Yeyun Gong, A Jin, Hang Zhang, Chen Lin, Jian Jiao, Siu Ming Yiu, Nan Duan, Weizhu Chen, et al. 2023 · 2023
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Unnatural instructions: Tuning language models with (almost) no human labor
Or Honovich, Thomas Scialom, Omer Levy, and Timo Schick. 2023a · 2023
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Towards reasoning in large language models: A survey
Jie Huang and Kevin Chen-Chuan Chang. 2023 · 2023
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HINT: hypernetwork instruction tuning for efficient zero- and few-shot generalisation
Hamish Ivison, Akshita Bhagia, Yizhong Wang, Hannaneh Hajishirzi, and Matthew E. Peters. 2023a · 2023
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Exploring the benefits of training expert language models over instruction tuning
Joel Jang, Seungone Kim, Seonghyeon Ye, Doyoung Kim, Lajanugen Logeswaran, Moontae Lee, Kyungjae Lee, and Minjoon Seo. 2023 · 2023
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Exploiting programmatic behavior of llms: Dual-use through standard security attacks
Daniel Kang, Xuechen Li, Ion Stoica, Carlos Guestrin, Matei Zaharia, and Tatsunori Hashimoto. 2023 · 2023
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Turning english-centric llms into polyglots: How much multilinguality is needed?
Tannon Kew, Florian Schottmann, and Rico Sennrich. 2023 · 2023
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The cot collection: Improving zero-shot and few-shot learning of language models via chain-of-thought fine-tuning
Seungone Kim, Se June Joo, Doyoung Kim, Joel Jang, Seonghyeon Ye, Jamin Shin, and Minjoon Seo. 2023 · 2023
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Longform: Optimizing instruction tuning for long text generation with corpus extraction
Abdullatif Köksal, Timo Schick, Anna Korhonen, and Hinrich Schütze. 2023 · 2023
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Openassistant conversations - democratizing large language model alignment
Andreas Köpf, Yannic Kilcher, Dimitri von Rütte, Sotiris Anagnostidis, Zhi Rui Tam, Keith Stevens, Abdullah Barhoum, Duc Nguyen, Oliver Stanley, Richárd Nagyfi, Shahul ES, Sameer Suri, David Glushkov, Arnav Dantuluri, Andrew Maguire, Christoph Schuhmann, Huu Nguyen, and Alexander Mattick. 2023 · 2023
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Finding supporting examples for in-context learning
Xiaonan Li and Xipeng Qiu. 2023 · 2023
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Scaling down to scale up: A guide to parameter-efficient fine-tuning
Vladislav Lialin, Vijeta Deshpande, and Anna Rumshisky. 2023 · 2023
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Ra-dit: Retrieval-augmented dual instruction tuning
Xi Victoria Lin, Xilun Chen, Mingda Chen, Weijia Shi, Maria Lomeli, Rich James, Pedro Rodriguez, Jacob Kahn, Gergely Szilvasy, Mike Lewis, et al. 2023 · 2023
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The flan collection: Designing data and methods for effective instruction tuning
Shayne Longpre, Le Hou, Tu Vu, Albert Webson, Hyung Won Chung, Yi Tay, Denny Zhou, Quoc V. Le, Barret Zoph, Jason Wei, and Adam Roberts. 2023 · 2023
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Forget demonstrations, focus on learning from textual instructions
Renze Lou and Wenpeng Yin. 2023 · 2023
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HELP ME THINK: A simple prompting strategy for non-experts to create customized content with models
Swaroop Mishra and Elnaz Nouri. 2023 · 2023
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Crosslingual generalization through multitask finetuning
Niklas Muennighoff, Thomas Wang, Lintang Sutawika, Adam Roberts, Stella Biderman, Teven Le Scao, M. Saiful Bari, Sheng Shen, Zheng Xin Yong, Hailey Schoelkopf, Xiangru Tang, Dragomir Radev, Alham Fikri Aji, Khalid Almubarak, Samuel Albanie, Zaid Alyafeai, Albert Webson, Edward Raff, and Colin Raffel. 2023 · 2023
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In-context example selection with influences
Tai Nguyen and Eric Wong. 2023 · 2023
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OpenAI. 2023 · 2023
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Non-proportional parametrizations for stable hypernetwork learning
Jose Javier Gonzalez Ortiz, John Guttag, and Adrian Dalca. 2023 · 2023
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Do the rewards justify the means? measuring trade-offs between rewards and ethical behavior in the machiavelli benchmark
Alexander Pan, Jun Shern Chan, Andy Zou, Nathaniel Li, Steven Basart, Thomas Woodside, Hanlin Zhang, Scott Emmons, and Dan Hendrycks. 2023 · 2023
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Don’t blame the annotator: Bias already starts in the annotation instructions
Mihir Parmar, Swaroop Mishra, Mor Geva, and Chitta Baral. 2023 · 2023
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Baolin Peng, Chunyuan Li, Pengcheng He, Michel Galley, and Jianfeng Gao. 2023 · 2023
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GrIPS: Gradient-free, edit-based instruction search for prompting large language models
Archiki Prasad, Peter Hase, Xiang Zhou, and Mohit Bansal. 2023 · 2023
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Measuring and narrowing the compositionality gap in language models
Ofir Press, Muru Zhang, Sewon Min, Ludwig Schmidt, Noah A Smith, and Mike Lewis. 2023 · 2023
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Reasoning with language model prompting: A survey
Shuofei Qiao, Yixin Ou, Ningyu Zhang, Xiang Chen, Yunzhi Yao, Shumin Deng, Chuanqi Tan, Fei Huang, and Huajun Chen. 2023 · 2023
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Coedit: Text editing by task-specific instruction tuning
Vipul Raheja, Dhruv Kumar, Ryan Koo, and Dongyeop Kang. 2023 · 2023
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Branch-solve-merge improves large language model evaluation and generation
Swarnadeep Saha, Omer Levy, Asli Celikyilmaz, Mohit Bansal, Jason Weston, and Xian Li. 2023 · 2023
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Dynamics of instruction tuning: Each ability of large language models has its own growth pace
Chiyu Song, Zhanchao Zhou, Jianhao Yan, Yuejiao Fei, Zhenzhong Lan, and Yue Zhang. 2023 · 2023
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One embedder, any task: Instruction-finetuned text embeddings
Hongjin Su, Weijia Shi, Jungo Kasai, Yizhong Wang, Yushi Hu, Mari Ostendorf, Wen-tau Yih, Noah A. Smith, Luke Zettlemoyer, and Tao Yu. 2023 · 2023
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Challenging big-bench tasks and whether chain-of-thought can solve them
Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc V. Le, Ed H. Chi, Denny Zhou, and Jason Wei. 2023 · 2023
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Stanford alpaca: An instruction-following llama model
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023 · 2023
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UL2: unifying language learning paradigms
Yi Tay, Mostafa Dehghani, Vinh Q. Tran, Xavier Garcia, Jason Wei, Xuezhi Wang, Hyung Won Chung, Dara Bahri, Tal Schuster, Huaixiu Steven Zheng, Denny Zhou, Neil Houlsby, and Donald Metzler. 2023 · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. 2023 · 2023
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Poisoning language models during instruction tuning
Alexander Wan, Eric Wallace, Sheng Shen, and Dan Klein. 2023 · 2023
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Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V. Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2023b · 2023
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How far can camels go? exploring the state of instruction tuning on open resources
Yizhong Wang, Hamish Ivison, Pradeep Dasigi, Jack Hessel, Tushar Khot, Khyathi Chandu, David Wadden, Kelsey MacMillan, Noah A. Smith, Iz Beltagy, and Hannaneh Hajishirzi. 2023c · 2023
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Self-instruct: Aligning language models with self-generated instructions
Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, and Hannaneh Hajishirzi. 2023d · 2023
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Symbol tuning improves in-context learning in language models
Jerry W. Wei, Le Hou, Andrew K. Lampinen, Xiangning Chen, Da Huang, Yi Tay, Xinyun Chen, Yifeng Lu, Denny Zhou, Tengyu Ma, and Quoc V. Le. 2023 · 2023
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Self-adaptive in-context learning: An information compression perspective for in-context example selection and ordering
Zhiyong Wu, Yaoxiang Wang, Jiacheng Ye, and Lingpeng Kong. 2023 · 2023
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Baize: An open-source chat model with parameter-efficient tuning on self-chat data
Canwen Xu, Daya Guo, Nan Duan, and Julian J. McAuley. 2023b · 2023
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A universal discriminator for zero-shot generalization
Haike Xu, Zongyu Lin, Jing Zhou, Yanan Zheng, and Zhilin Yang. 2023d · 2023
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INSTRUCTSCORE: Towards explainable text generation evaluation with automatic feedback
Wenda Xu, Danqing Wang, Liangming Pan, Zhenqiao Song, Markus Freitag, William Wang, and Lei Li. 2023e · 2023
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Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan. 2023 · 2023
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In-context instruction learning
Seonghyeon Ye, Hyeonbin Hwang, Sohee Yang, Hyeongu Yun, Yireun Kim, and Minjoon Seo. 2023 · 2023
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Dynosaur: A dynamic growth paradigm for instruction-tuning data curation
Da Yin, Xiao Liu, Fan Yin, Ming Zhong, Hritik Bansal, Jiawei Han, and Kai-Wei Chang. 2023 · 2023
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Aligning instruction tasks unlocks large language models as zero-shot relation extractors
Kai Zhang, Bernal Jimenez Gutierrez, and Yu Su. 2023a · 2023
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LIMA: less is more for alignment
Chunting Zhou, Pengfei Liu, Puxin Xu, Srinivasan Iyer, Jiao Sun, Yuning Mao, Xuezhe Ma, Avia Efrat, Ping Yu, Lili Yu, Susan Zhang, Gargi Ghosh, Mike Lewis, Luke Zettlemoyer, and Omer Levy. 2023a · 2023
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Graph of thoughts: Solving elaborate problems with large language models
Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Michal Podstawski, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Hubert Niewiadomski, Piotr Nyczyk, et al. 2024 · 2024
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MUFFIN: Curating multi-faceted instructions for improving instruction following
Renze Lou, Kai Zhang, Jian Xie, Yuxuan Sun, Janice Ahn, Hanzi Xu, Yu su, and Wenpeng Yin. 2024 · 2024
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UMIE: unified multimodal information extraction with instruction tuning
Lin Sun, Kai Zhang, Qingyuan Li, and Renze Lou. 2024 · 2024
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Lamini-lm: A diverse herd of distilled models from large-scale instructions
Minghao Wu, Abdul Waheed, Chiyu Zhang, Muhammad Abdul-Mageed, and Alham Fikri Aji. 2024 · 2024
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