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 https://github.com/joeljang/ELM.

翻译：近期，在多任务上经过指令微调的语言模型（即多任务提示微调，MT）展现出对未见任务的泛化能力。先前研究表明，增加训练任务数量是提升MT语言模型性能的关键。然而，本研究报告了一个意外发现：仅在单一任务上微调的专家语言模型，在11个未见数据集和13个BIG-bench基准数据集上的平均准确率，分别比经过300多个不同任务训练的MT语言模型高出3.20%和1.29%。这一发现对先前认为"仅通过增加任务数量即可增强MT语言模型性能"的观点提出了质疑。基于此发现，我们进一步表明：为每个训练任务单独训练专家语言模型（而非单个零样本推理的MT语言模型）这一分布式方法具有多重优势，包括（1）避免指令微调中常见的负迁移效应，（2）无需重训练先前任务即可持续学习新任务以规避灾难性遗忘，以及（3）合并各专家模型时展现出组合能力。相关代码已在https://github.com/joeljang/ELM 开源。