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Found 4 papers, 2 papers with code
Why Larger Language Models Do In-context Learning Differently?
This sheds light on where transformers pay attention to and how that affects ICL.
Towards Few-Shot Adaptation of Foundation Models via Multitask Finetuning
An emerging solution with recent success in vision and NLP involves finetuning a foundation model on a selection of relevant tasks, before its adaptation to a target task with limited labeled samples.
Interfacing Foundation Models' Embeddings
To further unleash the power of foundation models, we present FIND, a generalized interface for aligning foundation models' embeddings with unified image and dataset-level understanding spanning modality and granularity.
A Theoretical Analysis on Feature Learning in Neural Networks: Emergence from Inputs and Advantage over Fixed Features
These results provide theoretical evidence showing that feature learning in neural networks depends strongly on the input structure and leads to the superior performance.
