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Performance of large language models (LLMs) may vary with different prompts or instructions of even the same task.
Language models are few-shot learners
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Automatically constructing a corpus of sentential paraphrases
Bill Dolan and Chris Brockett. 2005 · 2005
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Learning word vectors for sentiment analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. 2011 · 2011
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Fast and scalable decoding with language model look-ahead for phrase-based statistical machine translation
Joern Wuebker, Hermann Ney, and Richard Zens. 2012 · 2012
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts. 2013 · 2013
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Dbpedia–a large-scale, multilingual knowledge base extracted from wikipedia
Jens Lehmann, Robert Isele, Max Jakob, Anja Jentzsch, Dimitris Kontokostas, Pablo N Mendes, Sebastian Hellmann, Mohamed Morsey, Patrick Van Kleef, Sören Auer, et al. 2015 · 2015
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015 · 2015
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Diversity driven attention model for query-based abstractive summarization
Preksha Nema, Mitesh M. Khapra, Anirban Laha, and Balaraman Ravindran. 2017 · 2017
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Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers
Ji Gao, Jack Lanchantin, Mary Lou Soffa, and Yanjun Qi. 2018 · 2018
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A deep generative framework for paraphrase generation
Ankush Gupta, Arvind Agarwal, Prawaan Singh, and Piyush Rai. 2018 · 2018
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Adversarial example generation with syntactically controlled paraphrase networks
Mohit Iyyer, John Wieting, Kevin Gimpel, and Luke Zettlemoyer. 2018 · 2018
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Paraphrase generation with deep reinforcement learning
Zichao Li, Xin Jiang, Lifeng Shang, and Hang Li. 2018 · 2018
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Multi-source social feedback of online news feeds
Nuno Moniz and Luís Torgo. 2018 · 2018
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CARER: Contextualized affect representations for emotion recognition
Elvis Saravia, Hsien-Chi Toby Liu, Yen-Hao Huang, Junlin Wu, and Yi-Shin Chen. 2018 · 2018
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. 2018 · 2018
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Controllable paraphrase generation with a syntactic exemplar
Mingda Chen, Qingming Tang, Sam Wiseman, and Kevin Gimpel. 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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Decomposable neural paraphrase generation
Zichao Li, Xin Jiang, Lifeng Shang, and Qun Liu. 2019 · 2019
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Neural network acceptability judgments
Alex Warstadt, Amanpreet Singh, and Samuel R. Bowman. 2019 · 2019
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TweetEval: Unified benchmark and comparative evaluation for tweet classification
Francesco Barbieri, Jose Camacho-Collados, Luis Espinosa Anke, and Leonardo Neves. 2020 · 2020
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Neural syntactic preordering for controlled paraphrase generation
Tanya Goyal and Greg Durrett. 2020 · 2020
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How can we know what language models know?
Zhengbao Jiang, Frank F. Xu, Jun Araki, and Graham Neubig. 2020 · 2020
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Syntax-guided controlled generation of paraphrases
Ashutosh Kumar, Kabir Ahuja, Raghuram Vadapalli, and Partha Talukdar. 2020 · 2020
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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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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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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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Bertscore: Evaluating text generation with bert
Tianyi Zhang*, Varsha Kishore*, Felix Wu*, Kilian Q. Weinberger, and Yoav Artzi. 2020 · 2020
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Super-NaturalInstructions: Generalization via declarative instructions on 1600+ NLP tasks
Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, Atharva Naik, Arjun Ashok, Arut Selvan Dhanasekaran, Anjana Arunkumar, David Stap, Eshaan Pathak, Giannis Karamanolakis, Haizhi Lai, Ishan Purohit, Ishani Mondal, Jacob Anderson, Kirby Kuznia, Krima Doshi, Kuntal Kumar Pal, Maitreya Patel, Mehrad Moradshahi, Mihir Parmar, Mirali Purohit, Neeraj Varshney, Phani Rohitha Kaza, Pulkit Verma, Ravsehaj Singh Puri, Rushang Karia, Savan Doshi, Shailaja Keyur Sampat, Siddhartha Mishra, Sujan Reddy A, Sumanta Patro, Tanay Dixit, and Xudong Shen. 2022 · 2022
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Do prompt-based models really understand the meaning of their prompts?
Albert Webson and Ellie Pavlick. 2022 · 2022
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Large language models are human-level prompt engineers
Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, and Jimmy Ba. 2022 · 2022
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023 · 2023
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Generative pre-training for paraphrase generation by representing and predicting spans in exemplars
Tien-Cuong Bui, Van-Duc Le, Hai-Thien To, and Sang Kyun Cha. 2021 · 2021
Cited alongside, same era.
Making pre-trained language models better few-shot learners
Tianyu Gao, Adam Fisch, and Danqi Chen. 2021 · 2021
Cited alongside, same era.
Factorising meaning and form for intent-preserving paraphrasing
Tom Hosking and Mirella Lapata. 2021 · 2021
Cited alongside, same era.
Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang. 2021 · 2021
Cited alongside, same era.
ConRPG: Paraphrase generation using contexts as regularizer
Yuxian Meng, Xiang Ao, Qing He, Xiaofei Sun, Qinghong Han, Fei Wu, Chun Fan, and Jiwei Li. 2021 · 2021
Cited alongside, same era.
Multitask prompted training enables zero-shot task generalization
Victor Sanh, Albert Webson, Colin Raffel, Stephen H Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, et al. 2021 · 2021
Cited alongside, same era.
It’s not just size that matters: Small language models are also few-shot learners
Timo Schick and Hinrich Schütze. 2021 · 2021
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Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality
Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E Gonzalez, et al. 2023 · 2023
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Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. 2023 · 2023
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Demystifying prompts in language models via perplexity estimation
Hila Gonen, Srini Iyer, Terra Blevins, Noah Smith, and Luke Zettlemoyer. 2023 · 2023
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Affective and dynamic beam search for story generation
Tenghao Huang, Ehsan Qasemi, Bangzheng Li, He Wang, Faeze Brahman, Muhao Chen, and Snigdha Chaturvedi. 2023 · 2023
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In-context demonstration selection with cross entropy difference
Dan Iter, Reid Pryzant, Ruochen Xu, Shuohang Wang, Yang Liu, Yichong Xu, and Chenguang Zhu. 2023 · 2023
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Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al. 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, Nguyen Minh Duc, Oliver Stanley, Richárd Nagyfi, et al. 2023 · 2023
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CTQScorer: Combining multiple features for in-context example selection for machine translation
Aswanth Kumar, Ratish Puduppully, Raj Dabre, and Anoop Kunchukuttan. 2023 · 2023
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Crafting in-context examples according to lms’ parametric knowledge
Yoonsang Lee, Pranav Atreya, Xi Ye, and Eunsol Choi. 2023 · 2023
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Contrastive decoding: Open-ended text generation as optimization
Xiang Lisa Li, Ari Holtzman, Daniel Fried, Percy Liang, Jason Eisner, Tatsunori Hashimoto, Luke Zettlemoyer, and Mike Lewis. 2023 · 2023
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Prismer: A vision-language model with multi-task experts
Shikun Liu, Linxi Fan, Edward Johns, Zhiding Yu, Chaowei Xiao, and Anima Anandkumar. 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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Automatic prompt augmentation and selection with chain-of-thought from labeled data
KaShun Shum, Shizhe Diao, and Tong Zhang. 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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ReadPrompt: A readable prompting method for reliable knowledge probing
Zezhong Wang, Luyao Ye, Hongru Wang, Wai-Chung Kwan, David Ho, and Kam-Fai Wong. 2023 · 2023
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Look-back decoding for open-ended text generation
Nan Xu, Chunting Zhou, Asli Celikyilmaz, and Xuezhe Ma. 2023 · 2023
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Llama 3 model card
AI@Meta. 2024 · 2024
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Famicom: Further demystifying prompts for language models with task-agnostic performance estimation
Bangzheng Li, Ben Zhou, Xingyu Fu, Fei Wang, Dan Roth, and Muhao Chen. 2024 · 2024
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Contrastive instruction tuning
Tianyi Yan, Fei Wang, James Y. Huang, Wenxuan Zhou, Fan Yin, Aram Galstyan, Wenpeng Yin, and Muhao Chen. 2024 · 2024
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