Instruction Matters, a Simple yet Effective Task Selection Approach in Instruction Tuning for Specific Tasks
Instruction tuning has shown its ability to not only enhance zero-shot generalization across various tasks but also its effectiveness in improving the performance of specific tasks. A crucial aspect in instruction tuning for a particular task is a strategic selection of related tasks that offer meaningful supervision, thereby enhancing efficiency and preventing performance degradation from irrelevant tasks. Our research reveals that leveraging instruction information \textit{alone} enables the identification of pertinent tasks for instruction tuning. This approach is notably simpler compared to traditional methods that necessitate complex measurements of pairwise transferability between tasks or the creation of data samples for the target task. Furthermore, by additionally learning the unique instructional template style of the meta-dataset, we observe an improvement in task selection accuracy, which contributes to enhanced overall performance. Experimental results demonstrate that training on a small set of tasks, chosen solely based on the instructions, leads to substantial performance improvements on benchmarks like P3, Big-Bench, NIV2, and Big-Bench Hard. Significantly, these improvements exceed those achieved by prior task selection methods, highlighting the efficacy of our approach.
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BIG-bench
Meta-Dataset
