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Prompts have been proven to play a crucial role in large language models, and in recent years, vision models have also been using prompts to improve scalability for multiple downstream tasks.
Crafting papers on machine learning
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mixup: Beyond empirical risk minimization
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Language models are few-shot learners
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P-tuning: Prompt tuning can be comparable to fine-tuning across scales and tasks
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Ppt: Pre-trained prompt tuning for few-shot learning
Gu, Y., Han, X., Liu, Z., and Huang, M · 2021
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Rethinking spatial dimensions of vision transformers
Heo, B., Yun, S., Han, D., Chun, S., Choe, J., and Oh, S. J · 2021
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Scaling up visual and vision-language representation learning with noisy text supervision
Jia, C., Yang, Y., Xia, Y., Chen, Y.-T., Parekh, Z., Pham, H., Le, Q., Sung, Y.-H., Li, Z., and Duerig, T · 2021
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The power of scale for parameter-efficient prompt tuning
Lester, B., Al-Rfou, R., and Constant, N · 2021
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Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al · 2021
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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
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Flamingo: a visual language model for few-shot learning
Alayrac, J.-B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., et al · 2022
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Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W.-Y., et al · 2023
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Gpt-4 technical report, 2023
OpenAI · 2023
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Peng, B., Li, C., He, P., Galley, M., and Gao, J · 2023
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Stanford alpaca: An instruction-following llama model
Taori, R., Gulrajani, I., Zhang, T., Dubois, Y., Li, X., Guestrin, C., Liang, P., and Hashimoto, T. B · 2023
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Llama: Open and efficient foundation language models
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al · 2023
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Efficiently aligned cross-lingual transfer learning for conversational tasks using prompt-tuning
Tu, L., Qu, J., Yavuz, S., Joty, S., Liu, W., Xiong, C., and Zhou, Y · 2023
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What makes good examples for visual in-context learning?
Zhang, Y., Zhou, K., and Liu, Z · 2023
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When brain-inspired ai meets agi
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