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Recent progress in scaling up large language models has shown impressive capabilities in performing few-shot learning across a wide range of text-based tasks.
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Neural discrete representation learning
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Attention is all you need
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Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning
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Roberta: A robustly optimized bert pretraining approach
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Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks
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Exploring the limits of transfer learning with a unified text-to-text transformer
Roberts, A., Raffel, C., Lee, K., Matena, M., Shazeer, N., Liu, P. J., Narang, S., Li, W., and Zhou, Y · 2019
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Vl-bert: Pre-training of generic visual-linguistic representations
Su, W., Zhu, X., Cao, Y., Li, B., Lu, L., Wei, F., and Dai, J · 2019
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Encoder-agnostic adaptation for conditional language generation
Ziegler, Z. M., Melas-Kyriazi, L., Gehrmann, S., and Rush, A. M · 2019
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Language models are few-shot learners
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An image is worth 16x16 words: Transformers for image recognition at scale
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Flamingo: a visual language model for few-shot learning
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Visualgpt: Data-efficient adaptation of pretrained language models for image captioning
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Palm: Scaling language modeling with pathways
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Stylegan-nada: Clip-guided domain adaptation of image generators
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Unifying vision-and-language tasks via text generation
Cho, J., Lei, J., Tan, H., and Bansal, M · 2021
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Pretrained transformers as universal computation engines
Lu, K., Grover, A., Abbeel, P., and Mordatch, I · 2021
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Sdedit: Image synthesis and editing with stochastic differential equations
Meng, C., Song, Y., Song, J., Wu, J., Zhu, J.-Y., and Ermon, S · 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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Multimodal few-shot learning with frozen language models
Tsimpoukelli, M., Menick, J. L., Cabi, S., Eslami, S., Vinyals, O., and Hill, F · 2021
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Do prompt-based models really understand the meaning of their prompts?
Webson, A. and Pavlick, E · 2021
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Vector-quantized image modeling with improved vqgan
Yu, J., Li, X., Koh, J. Y., Zhang, H., Pang, R., Qin, J., Ku, A., Xu, Y., Baldridge, J., and Wu, Y · 2021
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Lampinen, A. K., Dasgupta, I., Chan, S. C., Matthewson, K., Tessler, M. H., Creswell, A., McClelland, J. L., Wang, J. X., and Hill, F · 2022
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Rethinking the role of demonstrations: What makes in-context learning work?
Min, S., Lyu, X., Holtzman, A., Artetxe, M., Lewis, M., Hajishirzi, H., and Zettlemoyer, L · 2022
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Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al · 2022
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Hierarchical text-conditional image generation with clip latents
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M · 2022
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Scaling autoregressive models for content-rich text-to-image generation
Yu, J., Xu, Y., Koh, J. Y., Luong, T., Baid, G., Wang, Z., Vasudevan, V., Ku, A., Yang, Y., Ayan, B. K., et al · 2022
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Opt: Open pre-trained transformer language models
Zhang, S., Roller, S., Goyal, N., Artetxe, M., Chen, M., Chen, S., Dewan, C., Diab, M., Li, X., Lin, X. V., Mihaylov, T., Ott, M., Shleifer, S., Shuster, K., Simig, D., Koura, P. S., Sridhar, A., Wang, T., and Zettlemoyer, L · 2022
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Muse: Text-to-image generation via masked generative transformers
Chang, H., Zhang, H., Barber, J., Maschinot, A., Lezama, J., Jiang, L., Yang, M.-H., Murphy, K., Freeman, W. T., Rubinstein, M., et al · 2023
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