[34] Hunter Lang, Aravindan Vijayaraghavan, and David Sontag. Training subset selection for weak supervision, 2022.
[35] Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang. Biobert: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4):1234-1240, 2020.
[36] Brian Lester, Rami Al-Rfou, and Noah Constant. The power of scale for parameter-efficient prompt tuning, 2021.
[37] Xiang Lisa Li and Percy Liang. Prefix-tuning: Optimizing continuous prompts for generation, 2021.
[38] Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, et al. Holistic evaluation of language models. arXiv preprint arXiv:2211.09110, 2022.
[39] Opher Lieber, Or Sharir, Barak Lenz, and Yoav Shoham. Jurassic-1: Technical details and evaluation. White Paper. AI21 Labs, 1, 2021.
[40] Lucy H Lin and Noah A Smith. Situating sentence embeddings with nearest neighbor overlap. arXiv preprint arXiv:1909.10724, 2019.
[41] Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Computing Surveys, 55(9):1-35, 2023.
[42] Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692, 2019.
[43] Kevin Meng, David Bau, Alex J Andonian, and Yonatan Belinkov. Locating and editing factual associations in gpt. In Advances in Neural Information Processing Systems, 2022.
[44] Kevin Meng, Arnab Sen Sharma, Alex Andonian, Yonatan Belinkov, and David Bau. Mass-editing memory in a transformer. arXiv preprint arXiv:2210.07229, 2022.
[45] Swaroop Mishra, Daniel Khashabi, Chitta Baral, Yejin Choi, and Hannaneh Hajishirzi. Reframing instructional prompts to gptk's language. arXiv preprint arXiv:2109.07830, 2021.
[46] Eric Mitchell, Charles Lin, Antoine Bosselut, Christopher D Manning, and Chelsea Finn. Memory-based model editing at scale. In International Conference on Machine Learning, pages 15817-15831. PMLR, 2022.
[47] Akihiro Nakamura and Tatsuya Harada. Revisiting fine-tuning for few-shot learning. arXiv preprint arXiv:1910.00216, 2019.
[48] OpenAI. Gpt-4 technical report, 2023.
[49] Laurel Orr. Manifest. https://github.com/HazyResearch/manifest, 2022.
[50] Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. arXiv preprint arXiv:2203.02155, 2022.
[51] Ethan Perez, Douwe Kiela, and Kyunghyun Cho. True few-shot learning with language models. Advances in neural information processing systems, 34:11054-11070, 2021.
[52] Rattana Pukdee, Dylan Sam, Maria-Florina Balcan, and Pradeep Ravikumar. Label propagation with weak supervision. arXiv preprint arXiv:2210.03594, 2022.
[53] A. J. Ratner, B. Hancock, J. Dunnmon, F. Sala, S. Pandey, and C. Ré. Training complex models with multi-task weak supervision. In Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, Hawaii, 2019.