Fetching the paper…
Reading the bibliography…
Large language models (LLMs) have been recently leveraged as training data generators for various natural language processing (NLP) tasks.
Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification
John Blitzer, Mark Dredze, and Fernando Pereira · 2007
Earlier work this paper cites.
Learning word vectors for sentiment analysis
Andrew Maas, Raymond E Daly, Peter T Pham, Dan Huang, Andrew Y Ng, and Christopher Potts · 2011
Earlier work this paper cites.
Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Y Ng, and Christopher Potts · 2013
Earlier work this paper cites.
Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun · 2015
Earlier work this paper cites.
Content preserving text generation with attribute controls
Lajanugen Logeswaran, Honglak Lee, and Samy Bengio · 2018
Earlier work this paper cites.
On the use of arxiv as a dataset
Colin B Clement, Matthew Bierbaum, Kevin P O’Keeffe, and Alexander A Alemi · 2019
Earlier work this paper cites.
Unsupervised cross-lingual representation learning at scale
Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov · 2019
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Earlier work this paper cites.
Breaking the glass ceiling for embedding-based classifiers for large output spaces
Chuan Guo, Ali Mousavi, Xiang Wu, Daniel N Holtmann-Rice, Satyen Kale, Sashank Reddi, and Sanjiv Kumar · 2019
Earlier work this paper cites.
Unifying human and statistical evaluation for natural language generation
Tatsunori B. Hashimoto, Hugh Zhang, and Percy Liang · 2019
Earlier work this paper cites.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
Earlier work this paper cites.
Weakly-supervised hierarchical text classification
Yu Meng, Jiaming Shen, Chao Zhang, and Jiawei Han · 2019
Earlier work this paper cites.
When does label smoothing help?
Rafael Müller, Simon Kornblith, and Geoffrey E Hinton · 2019
Earlier work this paper cites.
Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych · 2019
Earlier work this paper cites.
Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf · 2019
Earlier work this paper cites.
Symmetric cross entropy for robust learning with noisy labels
Yisen Wang, Xingjun Ma, Zaiyi Chen, Yuan Luo, Jinfeng Yi, and James Bailey · 2019
Earlier work this paper cites.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Earlier work this paper cites.
Tinybert: Distilling bert for natural language understanding
Xiaoqi Jiao, Yichun Yin, Lifeng Shang, Xin Jiang, Xiao Chen, Linlin Li, Fang Wang, and Qun Liu · 2020
Earlier work this paper cites.
Dqi: Measuring data quality in nlp
Swaroop Mishra, Anjana Arunkumar, Bhavdeep Sachdeva, Chris Bryan, and Chitta Baral · 2020
Earlier work this paper cites.
Control, generate, augment: A scalable framework for multi-attribute text generation
Giuseppe Russo, Nora Hollenstein, Claudiu Musat, and Ce Zhang · 2020
Earlier work this paper cites.
RAFT: A real-world few-shot text classification benchmark
Neel Alex, Eli Lifland, Lewis Tunstall, Abhishek Thakur, Pegah Maham, C. Jess Riedel, Emmie Hine, Carolyn Ashurst, Paul Sedille, Alexis Carlier, Michael Noetel, and Andreas Stuhlmüller · 2021
Earlier work this paper cites.
Simcse: Simple contrastive learning of sentence embeddings
Tianyu Gao, Xingcheng Yao, and Danqi Chen · 2021
Earlier work this paper cites.
Tweac: transformer with extendable qa agent classifiers
Gregor Geigle, Nils Reimers, Andreas Rücklé, and Iryna Gurevych · 2021
Cited alongside, same era.
Bias out-of-the-box: An empirical analysis of intersectional occupational biases in popular generative language models
Hannah Rose Kirk, Yennie Jun, Haider Iqbal, Elias Benussi, Filippo Volpin, Frédéric A. Dreyer, Aleksandar Shtedritski, and Yuki M. Asano · 2021
Cited alongside, same era.
True few-shot learning with language models
Ethan Perez, Douwe Kiela, and Kyunghyun Cho · 2021
Cited alongside, same era.
Prompt programming for large language models: Beyond the few-shot paradigm
Laria Reynolds and Kyle McDonell · 2021
Cited alongside, same era.
Generating datasets with pretrained language models
Timo Schick and Hinrich Schütze · 2021
Cited alongside, same era.
Opt: Open pre-trained transformer language models
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al · 2022
Later among the works it cites.
Yejin Bang, Samuel Cahyawijaya, Nayeon Lee, Wenliang Dai, Dan Su, Bryan Wilie, Holy Lovenia, Ziwei Ji, Tiezheng Yu, et al · 2023
Closest in time.
Language models are realistic tabular data generators
Vadim Borisov, Kathrin Sessler, Tobias Leemann, Martin Pawelczyk, and Gjergji Kasneci · 2023
Closest in time.
Assessing cross-cultural alignment between ChatGPT and human societies: An empirical study
Yong Cao, Li Zhou, Seolhwa Lee, Laura Cabello, Min Chen, and Daniel Hershcovich · 2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Taxoclass: Hierarchical multi-label text classification using only class names
Jiaming Shen, Wenda Qiu, Yu Meng, Jingbo Shang, Xiang Ren, and Jiawei Han · 2021
Cited alongside, same era.
Evaluating the evaluation of diversity in natural language generation
Guy Tevet and Jonathan Berant · 2021
Cited alongside, same era.
Attribute alignment: Controlling text generation from pre-trained language models
Dian Yu, Zhou Yu, and Kenji Sagae · 2021
Cited alongside, same era.
Relationprompt: Leveraging prompts to generate synthetic data for zero-shot relation triplet extraction
Yew Ken Chia, Lidong Bing, Soujanya Poria, and Luo Si · 2022
Cited alongside, same era.
Fine-tuning can distort pretrained features and underperform out-of-distribution
Ananya Kumar, Aditi Raghunathan, Robbie Matthew Jones, Tengyu Ma, and Percy Liang · 2022
Cited alongside, same era.
HERB: Measuring hierarchical regional bias in pre-trained language models
Yizhi Li, Ge Zhang, Bohao Yang, Chenghua Lin, Anton Ragni, Shi Wang, and Jie Fu · 2022
Cited alongside, same era.
Holistic evaluation of language models
Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, et al · 2022
Cited alongside, same era.
Derek Chen, Celine Lee, Yun-Yun Lu, Domenic Rosati, and Zhou Yu · 2023
Closest in time.
Datacomp: In search of the next generation of multimodal datasets
Samir Yitzhak Gadre, Gabriel Ilharco, Alex Fang, Jonathan Hayase, Georgios Smyrnis, Thao Nguyen, Ryan Marten, Mitchell Wortsman, Dhruba Ghosh, Jieyu Zhang, et al · 2023
Closest in time.
Self-guided noise-free data generation for efficient zero-shot learning
Jiahui Gao, Renjie Pi, Lin Yong, Hang Xu, Jiacheng Ye, Zhiyong Wu, Weizhong Zhang, Xiaodan Liang, Zhenguo Li, and Lingpeng Kong · 2023
Closest in time.
DeBERTav3: Improving deBERTa using ELECTRA-style pre-training with gradient-disentangled embedding sharing
Pengcheng He, Jianfeng Gao, and Weizhu Chen · 2023
Closest in time.
Decomposed prompting: A modular approach for solving complex tasks
Tushar Khot, Harsh Trivedi, Matthew Finlayson, Yao Fu, Kyle Richardson, Peter Clark, and Ashish Sabharwal · 2023
Closest in time.
Beyond one-model-fits-all: A survey of domain specialization for large language models
Chen Ling, Xujiang Zhao, Jiaying Lu, Chengyuan Deng, Can Zheng, Junxiang Wang, Tanmoy Chowdhury, Yun Li, Hejie Cui, Tianjiao Zhao, et al · 2023
Closest in time.
HELP ME THINK: A simple prompting strategy for non-experts to create customized content with models
Swaroop Mishra and Elnaz Nouri · 2023
Closest in time.
Gpt-4 technical report
OpenAI · 2023
Closest in time.
Introducing chatgpt, 2023
OpenAI · 2023
Closest in time.
Baolin Peng, Michel Galley, Pengcheng He, Hao Cheng, Yujia Xie, Yu Hu, Qiuyuan Huang, Lars Liden, Zhou Yu, et al · 2023
Closest in time.
Baolin Peng, Chunyuan Li, Pengcheng He, Michel Galley, and Jianfeng Gao · 2023
Closest in time.
Synthetic prompting: Generating chain-of-thought demonstrations for large language models
Zhihong Shao, Yeyun Gong, Yelong Shen, Minlie Huang, Nan Duan, and Weizhu Chen · 2023
Closest in time.
Principle-driven self-alignment of language models from scratch with minimal human supervision
Zhiqing Sun, Yikang Shen, Qinhong Zhou, Hongxin Zhang, Zhenfang Chen, David Cox, Yiming Yang, and Chuang Gan · 2023
Closest in time.
Regen: Zero-shot text classification via training data generation with progressive dense retrieval
Yue Yu, Yuchen Zhuang, Rongzhi Zhang, Yu Meng, Jiaming Shen, and Chao Zhang · 2023
Closest in time.
On the trade-off of intra-/inter-class diversity for supervised pre-training
Jieyu Zhang, Bohan Wang, Zhengyu Hu, Pang Wei Koh, and Alexander Ratner · 2023
Closest in time.
Large language models are human-level prompt engineers
Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, and Jimmy Ba · 2023
Closest in time.
Exploring ai ethics of chatgpt: A diagnostic analysis
Terry Yue Zhuo, Yujin Huang, Chunyang Chen, and Zhenchang Xing · 2023
Closest in time.