Lin, Pin-Jie; Zhang, Miaoran; Mosbach, Marius; Klakow, Dietrich
Exploring the Effectiveness and Consistency of Task Selection in Intermediate-Task Transfer Learning
Fu, Xiyan; Fleisig, Eve; (Ed.): Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop), Association for Computational Linguistics, pp. 170-185, Bangkok, Thailand, 2024, ISBN 979-8-89176-097-4.
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.