Actually, gradient descent can be seen as attention that applies beyond the model's context length!
Explains the working mechanism of In-Context Learning in large pretrained language models as a kind of implicit finetuning through Transformer attention that has a dual form of gradient descent based optimization. Offers insights for future model designing.
Can help in understanding how In-Context Learning works and provide insights for future model designing.
Zero-shot Image-to-Image Translation
Proposes pix2pix-zero, an image-to-image translation method that can preserve the content of the original image without manual prompting. Outperforms existing and concurrent works for both real and synthetic image editing.
Can improve image editing processes without the need for manual prompting and can outperform existing methods.
Languages are Rewards: Hindsight Finetuning using Human Feedback
Conditions the model on outputs paired with hindsight feedback and finetunes the model to predict the most preferred output, resulting in better performance on summarization and dialogue tasks.
Can improve language models by learning from any form of feedback, regardless of its polarity, and aligning with human preferences.