79 citations · 231 across the 15 of their papers we have counts for
9 papers · 1 filter
Frontier Language Models are not Robust to Adversarial Arithmetic, or "What do I need to say so you agree 2+2=5?
C. Daniel Freeman, Laura Culp, Aaron Parisi +27
We introduce and study the problem of adversarial arithmetic, which provides a simple yet challenging testbed for language model alignment. This problem is comprised of arithmetic…
Probing clustering in neural network representations
Thao Nguyen, Simon Kornblith
Neural network representations contain structure beyond what was present in the training labels. For instance, representations of images that are visually or semantically similar t…
Replacing softmax with ReLU in Vision Transformers
Mitchell Wortsman, Jaehoon Lee, Justin Gilmer +1
Previous research observed accuracy degradation when replacing the attention softmax with a point-wise activation such as ReLU. In the context of vision transformers, we find that…
Small-scale proxies for large-scale Transformer training instabilities
Mitchell Wortsman, Peter J. Liu, Lechao Xiao +13
Teams that have trained large Transformer-based models have reported training instabilities at large scale that did not appear when training with the same hyperparameters at smalle…
Getting aligned on representational alignment
Ilia Sucholutsky, Lukas Muttenthaler, Adrian Weller +30
Biological and artificial information processing systems form representations of the world that they can use to categorize, reason, plan, navigate, and make decisions. How can we m…
OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models
Anas Awadalla, Irena Gao, Josh Gardner +13
We introduce OpenFlamingo, a family of autoregressive vision-language models ranging from 3B to 9B parameters. OpenFlamingo is an ongoing effort to produce an open-source replicati…
Guiding Image Captioning Models Toward More Specific Captions
Simon Kornblith, Lala Li, Zirui Wang +1
Image captioning is conventionally formulated as the task of generating captions for images that match the distribution of reference image-caption pairs. However, reference caption…
Towards Generalist Biomedical AI
Tao Tu, Shekoofeh Azizi, Danny Driess +29
Medicine is inherently multimodal, with rich data modalities spanning text, imaging, genomics, and more. Generalist biomedical artificial intelligence (AI) systems that flexibly en…
Synthetic Data from Diffusion Models Improves ImageNet Classification
Shekoofeh Azizi, Simon Kornblith, Chitwan Saharia +2
Deep generative models are becoming increasingly powerful, now generating diverse high fidelity photo-realistic samples given text prompts. Have they reached the point where models…