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Large Language Models (LLMs), primarily trained on text-based datasets, exhibit exceptional proficiencies in understanding and executing complex linguistic instructions via text outputs.
Language models are few-shot learners
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Microsoft coco captions: Data collection and evaluation server
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
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Attngan: Fine-grained text to image generation with attentional generative adversarial networks
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A comprehensive survey of deep learning for image captioning
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Decoupled weight decay regularization
Loshchilov, I.; and Hutter, F. 2019 · 2019
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Language models are unsupervised multitask learners
Radford, A.; Wu, J.; Child, R.; Luan, D.; Amodei, D.; Sutskever, I.; et al. 2019 · 2019
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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 · 2019
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Libritts: A corpus derived from librispeech for text-to-speech
Zen, H.; Dang, V.; Clark, R.; Zhang, Y.; Weiss, R. J.; Jia, Y.; Chen, Z.; and Wu, Y. 2019 · 2019
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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 · 2020
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Measuring massive multitask language understanding
Hendrycks, D.; Burns, C.; Basart, S.; Zou, A.; Mazeika, M.; Song, D.; and Steinhardt, J. 2021 · 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 · 2021
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Zero-shot text-to-image generation
Ramesh, A.; Pavlov, M.; Goh, G.; Gray, S.; Voss, C.; Radford, A.; Chen, M.; and Sutskever, I. 2021 · 2021
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Speecht5: Unified-modal encoder-decoder pre-training for spoken language processing
Ao, J.; Wang, R.; Zhou, L.; Wang, C.; Ren, S.; Wu, Y.; Liu, S.; Ko, T.; Li, Q.; Zhang, Y.; et al. 2022 · 2022
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Palm: Scaling language modeling with pathways
Chowdhery, A.; Narang, S.; Devlin, J.; Bosma, M.; Mishra, G.; Roberts, A.; Barham, P.; Chung, H. W.; Sutton, C.; Gehrmann, S.; et al. 2022 · 2022
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Dettmers, T.; Lewis, M.; Belkada, Y.; and Zettlemoyer, L. 2022 · 2022
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Lora: Low-rank adaptation of large language models
Hu, E. J.; Shen, Y.; Wallis, P.; Allen-Zhu, Z.; Li, Y.; Wang, S.; Wang, L.; and Chen, W. 2022 · 2022
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Qlora: Efficient finetuning of quantized llms
Dettmers, T.; Pagnoni, A.; Holtzman, A.; and Zettlemoyer, L. 2023 · 2023
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Palm-e: An embodied multimodal language model
Driess, D.; Xia, F.; Sajjadi, M. S.; Lynch, C.; Chowdhery, A.; Ichter, B.; Wahid, A.; Tompson, J.; Vuong, Q.; Yu, T.; et al. 2023 · 2023
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Self-instruct: Aligning language model with self generated instructions
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Opt: Open pre-trained transformer language models
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One transformer fits all distributions in multi-modal diffusion at scale
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