通过多任务微调实现基础模型的少样本适配
Towards Few-Shot Adaptation of Foundation Models via Multitask Finetuning
AI总结：
本文从理论上证明在多样化相关任务上多任务微调基础模型可降低少样本目标任务的适配误差，并提出基于多样性与一致性指标的任务选择算法，实验验证了其有效性。
摘要：
基础模型已成为许多人工智能问题的强大工具。尽管基础模型取得了巨大成功，但如何有效适配新任务，尤其是标签有限的任务，仍然是一个悬而未决的问题，并且缺乏理论理解。近期在视觉和自然语言处理领域取得成功的解决方案是：在适配到只有少量标注样本的目标任务之前，先在一组选定的相关任务上对基础模型进行微调。本文研究了这种多任务微调方法的理论依据。我们的理论分析表明，与直接适配相同的预训练模型相比，在一组多样化的相关任务上进行多任务微调可以降低目标任务中的误差。我们通过多样性和一致性指标量化了微调任务与目标任务之间的关系，并进一步提出了一种实用的任务选择算法。我们用大量实证证据验证了理论主张。此外，我们给出的结果表明，我们的任务选择算法能够恰当地选择相关微调任务，从而提升模型在目标任务上的性能。我们相信，本研究为将基础模型有效适配到缺乏大量标签的新任务提供了新的见解。我们的代码可在 https://github.com/OliverXUZY/Foudation-Model_Multitask 获取。
英文摘要：
Foundation models have emerged as a powerful tool for many AI problems. Despite the tremendous success of foundation models, effective adaptation to new tasks, particularly those with limited labels, remains an open question and lacks theoretical understanding. 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. In this paper, we study the theoretical justification of this multitask finetuning approach. Our theoretical analysis reveals that with a diverse set of related tasks, this multitask finetuning leads to reduced error in the target task, in comparison to directly adapting the same pretrained model. We quantify the relationship between finetuning tasks and target tasks by diversity and consistency metrics, and further propose a practical task selection algorithm. We substantiate our theoretical claims with extensive empirical evidence. Further, we present results affirming our task selection algorithm adeptly chooses related finetuning tasks, providing advantages to the model performance on target tasks. We believe our study shed new light on the effective adaptation of foundation models to new tasks that lack abundant labels. Our code is available at https://github.com/OliverXUZY/Foudation-Model_Multitask.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
