Recently, Language Models (LMs) instruction-tuned on multiple tasks, also
known as multitask-prompted fine-tuning (MT), have shown the capability to
generalize to unseen tasks. Previous work has shown that scaling the number of
training tasks is the key component in making stronger MT LMs. In this work, we
report an unexpected finding that an expert LM fine-tuned on just a single task
can outperform an MT LM trained with 300+ different tasks on 11 different
unseen datasets and on 13 datasets of the BIG-bench benchmark by a mean
accuracy of 3.20% and 1.29%, respectively. This finding casts doubt on the
previously held belief that simply scaling the number of tasks makes stronger
MT LMs. Leveraging this finding, we further show that this distributed approach
of training a separate expert LM per training task instead of a single MT LM
for zero-shot inference possesses many benefits including (1) avoiding negative
task transfer that often occurs during instruction tuning, (2) being able to
continually learn new tasks without having to re-train on previous tasks to
avoid catastrophic forgetting, and (3) showing compositional capabilities when
merging individual experts together. The code is available at
this https URL.

本文研究发现，相对于使用多个任务的 multitask-prompted fine-tuning 方法进行指导调整的语言模型，仅针对单个任务进行专家模型的 fine-tuning 能够使模型在 11 个不同的未知数据集上以及 13 个 BIG-bench 基准测试数据集上平均准确率分别提高 3.20% 和 1.29%。此外，分别训练每个专家模型而不是单个 MT LM 进行 zero-shot 推断具有许多好处，包括避免负面任务转移，能够持续学习新任务而无需重新训练以避免灾难性遗忘以及显示在合并单个专家时具有组合能力。