指令至关重要：一种简单而有效的面向特定任务优化指令微调的任务选择方法
Instruction Matters: A Simple yet Effective Task Selection for Optimized Instruction Tuning of Specific Tasks
AI总结：
本文提出一种仅利用指令信息选择相关任务的简单高效方法，通过任务选择优化特定任务的指令微调，在多个基准上超越先前方法。
摘要：
指令微调已被证明能有效增强跨各种任务的零样本泛化能力，并提升特定任务的表现。对于特定任务的改进，策略性地选择并训练那些能提供有意义监督的相关任务至关重要，因为这种方法能提高效率，并防止因学习不相关任务而导致的性能下降。鉴于此，我们提出了一种简单而有效的任务选择方法，该方法仅利用指令信息来识别相关任务，从而优化针对特定任务的指令微调。与传统方法相比，我们的方法效率显著更高，因为传统方法需要复杂地测量任务之间的成对可迁移性，或为目标任务创建数据样本。此外，通过使模型与元数据集独特的指令模板风格对齐，我们增强了其细粒度辨别相关任务的能力，从而提升了整体性能。实验结果表明，仅基于指令选择的一小组任务上进行训练，就能在P3、Big-Bench、NIV2和Big-Bench Hard等基准上带来显著的性能提升。重要的是，这些提升超过了先前任务选择方法所取得的成果，凸显了我们方法的优越性。
英文摘要：
Instruction tuning has been proven effective in enhancing zero-shot generalization across various tasks and in improving the performance of specific tasks. For task-specific improvements, strategically selecting and training on related tasks that provide meaningful supervision is crucial, as this approach enhances efficiency and prevents performance degradation from learning irrelevant tasks. In this light, we introduce a simple yet effective task selection method that leverages instruction information alone to identify relevant tasks, optimizing instruction tuning for specific tasks. Our method is significantly more efficient than traditional approaches, which require complex measurements of pairwise transferability between tasks or the creation of data samples for the target task. Additionally, by aligning the model with the unique instructional template style of the meta-dataset, we enhance its ability to granularly discern relevant tasks, leading to improved overall performance. Experimental results demonstrate that training on a small set of tasks, chosen solely based on the instructions, results in substantial improvements in performance on benchmarks such as P3, Big-Bench, NIV2, and Big-Bench Hard. Significantly, these improvements surpass those achieved by prior task selection methods, highlighting the superiority of our approach.
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。
