A Google Research team conducts a systematic exploration comprising more than 4800 experiments on Vision Transformers, MLP-Mixers and ResNets with parameters ranging from 10 million to 10 billion, evaluated on more than 20 downstream image recognition tasks, aiming to capture the nonlinear relationships between performance on upstream and downstream tasks.
In a 200+ page paper, Percy Liang, Fei-Fei Li, and over 100 other researchers from the Stanford University Center for Research on Foundation Models (CRFM) systematically describe the opportunities and risks of large-scale pretrained “foundation” models. The unique study aims to provide a clearer understanding of how these models work, when and how they fail, and the various capabilities provided by their emergent properties.
A Google Research team proposes MergeDistill, a framework for merging pretrained teacher LMs from multiple monolingual/multilingual LMs into a single multilingual task-agnostic student LM to leverage the capabilities of the powerful language-specific LMs while still being multilingual and enabling positive language transfer.
A research team from Facebook shows how the power of transfer learning can enable pretraining on non-IDE, non-autocompletion and different-language example code sequences before fine-tuning on the autocompletion prediction task to improve model accuracy by over 50 percent on very small fine-tuning datasets and over 10 percent on 50k labelled examples.
A research team from Huawei Noah’s Ark Lab and Tsinghua University proposes Extract Then Distill (ETD), a generic and flexible strategy for reusing teacher model parameters for efficient and effective task-agnostic distillation that can be applied to student models of any size.