Exploring the Effectiveness and Consistency of Task Selection in Intermediate-Task Transfer Learning
Abstract
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.- Anthology ID:
- 2024.acl-srw.24
- Volume:
- Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Xiyan Fu, Eve Fleisig
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 264–279
- Language:
- URL:
- https://aclanthology.org/2024.acl-srw.24
- DOI:
- Cite (ACL):
- Pin-Jie Lin, Miaoran Zhang, Marius Mosbach, and Dietrich Klakow. 2024. Exploring the Effectiveness and Consistency of Task Selection in Intermediate-Task Transfer Learning. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop), pages 264–279, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- Exploring the Effectiveness and Consistency of Task Selection in Intermediate-Task Transfer Learning (Lin et al., ACL 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.acl-srw.24.pdf