This paper explores the theoretical justification and practical effectiveness of multitask finetuning for adapting foundation models to new tasks with limited labeled samples. The authors argue that finetuning a foundation model on a diverse set of relevant tasks before adapting it to a target task can improve performance compared to direct adaptation. They present a theoretical framework that analyzes the impact of diversity and consistency in the finetuning tasks on the target task's performance. The key findings include: 1. **Theoretical Analysis**: The authors derive theoretical guarantees showing that with limited labeled data from diverse tasks, multitask finetuning can reduce the error in the target task compared to direct adaptation. They introduce metrics for diversity and consistency and prove that a sufficiently diverse set of tasks can capture similar latent characteristics to the target task, leading to improved performance. 2. **Empirical Validation**: Extensive experiments on vision and language tasks validate the theoretical findings. The results show that multitask finetuning with diverse tasks consistently outperforms direct adaptation and standard finetuning. 3. **Task Selection Algorithm**: Inspired by the theoretical findings, the authors propose a practical task selection algorithm that selects tasks based on their diversity and consistency with the target task. This algorithm significantly improves the performance of the model on the target task, as demonstrated through experiments on the Meta-Dataset. The paper contributes to the understanding of effective adaptation of foundation models to new tasks with limited labels and provides a practical approach to selecting relevant tasks for multitask finetuning.This paper explores the theoretical justification and practical effectiveness of multitask finetuning for adapting foundation models to new tasks with limited labeled samples. The authors argue that finetuning a foundation model on a diverse set of relevant tasks before adapting it to a target task can improve performance compared to direct adaptation. They present a theoretical framework that analyzes the impact of diversity and consistency in the finetuning tasks on the target task's performance. The key findings include: 1. **Theoretical Analysis**: The authors derive theoretical guarantees showing that with limited labeled data from diverse tasks, multitask finetuning can reduce the error in the target task compared to direct adaptation. They introduce metrics for diversity and consistency and prove that a sufficiently diverse set of tasks can capture similar latent characteristics to the target task, leading to improved performance. 2. **Empirical Validation**: Extensive experiments on vision and language tasks validate the theoretical findings. The results show that multitask finetuning with diverse tasks consistently outperforms direct adaptation and standard finetuning. 3. **Task Selection Algorithm**: Inspired by the theoretical findings, the authors propose a practical task selection algorithm that selects tasks based on their diversity and consistency with the target task. This algorithm significantly improves the performance of the model on the target task, as demonstrated through experiments on the Meta-Dataset. The paper contributes to the understanding of effective adaptation of foundation models to new tasks with limited labels and provides a practical approach to selecting relevant tasks for multitask finetuning.