Exploring the Effectiveness and Consistency of Task Selection in Intermediate-Task Transfer Learning
<|reference_start|>Exploring the Effectiveness and Consistency of Task Selection in Intermediate-Task Transfer Learning: Identifying beneficial tasks to transfer from is a critical step toward successful intermediate-task transfer learning. In this work, we experiment with 130 source-target task combinations and demonstrate that the transfer performance exhibits severe variance across different source tasks and training seeds, highlighting the crucial role of intermediate-task selection in a broader context. We compare four representative task selection methods in a unified setup, focusing on their effectiveness and consistency. Compared to embedding-free methods and text embeddings, task embeddings constructed from fine-tuned weights can better estimate task transferability by improving task prediction scores from 2.59% to 3.96%. Despite their strong performance, we observe that the task embeddings do not consistently demonstrate superiority for tasks requiring reasoning abilities. Furthermore, we introduce a novel method that measures pairwise token similarity using maximum inner product search, leading to the highest performance in task prediction. Our findings suggest that token-wise similarity is better predictive for predicting transferability compared to averaging weights.<|reference_end|>
@article{lin2024exploring,
    title={Exploring the Effectiveness and Consistency of Task Selection in
  Intermediate-Task Transfer Learning},
    author={Pin-Jie Lin, Miaoran Zhang, Marius Mosbach, Dietrich Klakow},
    journal={arXiv preprint arXiv:2407.16245},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.16245},
    primaryClass={cs.CL}
}