Fetching the paper…
Reading the bibliography…
We demonstrate that co-training (Blum & Mitchell, 1998) can improve the performance of prompt-based learning by using unlabeled data.
Probability of error of some adaptive pattern-recognition machines
Scudder, H · 1965
Earlier work this paper cites.
Combining labeled and unlabeled data with co-training
Blum, A. and Mitchell, T · 1998
Earlier work this paper cites.
Building a question answering test collection
Voorhees, E. M. and Tice, D. M · 2000
Earlier work this paper cites.
True few-shot learning with prompts – a real-world perspective
Schick, T. and Schütze, H · 2001
Earlier work this paper cites.
Pac generalization bounds for co-training
Dasgupta, S., Littman, M. L., and McAllester, D · 2002
Earlier work this paper cites.
Identifying and handling mislabelled instances
Muhlenbach, F., Lallich, S., and Zighed, D. A · 2004
Earlier work this paper cites.
Co-training and expansion: Towards bridging theory and practice
Balcan, M.-F., Blum, A., and Yang, K · 2005
Earlier work this paper cites.
The pascal recognising textual entailment challenge
Dagan, I., Glickman, O., and Magnini, B · 2005
Earlier work this paper cites.
Cotrade: Confident co-training with data editing
Zhang, M.-L. and Zhou, Z.-H · 2011
Earlier work this paper cites.
Data programming: Creating large training sets, quickly
Ratner, A. J., De Sa, C. M., Wu, S., Selsam, D., and Ré, C · 2016
Earlier work this paper cites.
Wic: the word-in-context dataset for evaluating context-sensitive meaning representations
Pilehvar, M. T. and Camacho-Collados, J · 2018
Earlier work this paper cites.
BoolQ: Exploring the surprising difficulty of natural yes/no questions
Clark, C., Lee, K., Chang, M.-W., Kwiatkowski, T., Collins, M., and Toutanova, K · 2019
Earlier work this paper cites.
The commitmentbank: Investigating projection in naturally occurring discourse
De Marneffe, M.-C., Simons, M., and Tonhauser, J · 2019
Earlier work this paper cites.
Leveraging just a few keywords for fine-grained aspect detection through weakly supervised co-training
Karamanolakis, G., Hsu, D., and Gravano, L · 2019
Earlier work this paper cites.
Glue: A multi-task benchmark and analysis platform for natural language understanding
Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., and Bowman, S. R · 2019
Cited alongside, same era.
Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D · 2020
Cited alongside, same era.
Fast and three-rious: Speeding up weak supervision with triplet methods
Fu, D., Chen, M., Sala, F., Hooper, S., Fatahalian, K., and Ré, C · 2020
Cited alongside, same era.
How can we know what language models know?
Jiang, Z., Xu, F. F., Araki, J., and Neubig, G · 2020
Cited alongside, same era.
Uncertainty-aware self-training for few-shot text classification
Mukherjee, S. and Awadallah, A · 2020
Cited alongside, same era.
Reordering examples helps during priming-based few-shot learning
Kumar, S. and Talukdar, P · 2021
Later among the works it cites.
How many data points is a prompt worth?
Le Scao, T. and Rush, A · 2021
Later among the works it cites.
The power of scale for parameter-efficient prompt tuning
Lester, B., Al-Rfou, R., and Constant, N · 2021
Later among the works it cites.
Prefix-tuning: Optimizing continuous prompts for generation
Li, X. L. and Liang, P · 2021
Later among the works it cites.
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., and Neubig, G · 2021
Later among the works it cites.
Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., and Liu, P. J · 2020
Cited alongside, same era.
Snorkel: Rapid training data creation with weak supervision
Ratner, A., Bach, S. H., Ehrenberg, H., Fries, J., Wu, S., and Ré, C · 2020
Cited alongside, same era.
AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts
Shin, T., Razeghi, Y., Logan IV, R. L., Wallace, E., and Singh, S · 2020
Cited alongside, same era.
Theoretical analysis of self-training with deep networks on unlabeled data
Wei, C., Shen, K., Chen, Y., and Ma, T · 2020
Cited alongside, same era.
Transformers: State-of-the-art natural language processing
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., Davison, J., Shleifer, S., von Platen, P., Ma, C., Jernite, Y., Plu, J., Xu, C., Scao, T. L., Gugger, S., Drame, M., Lhoest, Q., and Rush, A. M · 2020
Cited alongside, same era.
Making pre-trained language models better few-shot learners
Gao, T., Fisch, A., and Chen, D · 2021
Cited alongside, same era.
Deberta: Decoding-enhanced bert with disentangled attention
He, P., Liu, X., Gao, J., and Chen, W · 2021
Cited alongside, same era.
Lu, Y., Bartolo, M., Moore, A., Riedel, S., and Stenetorp, P · 2021
Later among the works it cites.
True few-shot learning with language models
Perez, E., Kiela, D., and Cho, K · 2021
Later among the works it cites.
Exploiting cloze-questions for few-shot text classification and natural language inference
Schick, T. and Schütze, H · 2021
Later among the works it cites.
Want to reduce labeling cost? GPT-3 can help
Wang, S., Liu, Y., Xu, Y., Zhu, C., and Zeng, M · 2021
Later among the works it cites.
Finetuned language models are zero-shot learners
Wei, J., Bosma, M., Zhao, V. Y., Guu, K., Yu, A. W., Lester, B., Du, N., Dai, A. M., and Le, Q. V · 2021
Later among the works it cites.
Calibrate before use: Improving few-shot performance of language models
Zhao, Z., Wallace, E., Feng, S., Klein, D., and Singh, S · 2021
Later among the works it cites.
Machine learning core
Horng, S · 2022
Closest in time.
Multitask prompted training enables zero-shot task generalization
Sanh, V., Webson, A., Raffel, C., Bach, S., Sutawika, L., Alyafeai, Z., Chaffin, A., Stiegler, A., Le Scao, T., Raja, A., et al · 2022
Closest in time.