指令多样性驱动对未见任务的泛化
Instruction Diversity Drives Generalization To Unseen Tasks
AI总结：
本文通过字符串重写任务实验发现，指令集的多样性而非单任务样本量，是驱动大型语言模型向未见任务泛化及保证鲁棒性的关键因素。
摘要：
指令微调——在指令和期望结果的配对上微调大型语言模型（LLM）——是一种使预训练语言模型能够执行现实世界任务并遵循人类指令的方法。它的实际成功取决于模型学习比其训练时所用的指令更广泛指令集的能力。然而，决定模型向此类未见任务泛化的因素尚不清楚。在本文中，我们实验了字符串重写，这是一种符号任务，它作为图灵完备Markov算法的构建块，同时允许对“输入”和“指令”进行实验控制。我们研究了模型训练的指令数量与每条指令提供的训练样本数量之间的权衡，并观察到指令集的多样性决定了泛化能力。一旦提供了足够多样的任务集，泛化就会出现，即使每个任务只提供了很少的示例。指令多样性还确保了针对训练集中指令非均匀分布的鲁棒性。
英文摘要：
Instruction tuning -- fine-tuning a large language model (LLM) on pairs of instructions and desired outcomes -- is an approach that enables pre-trained language models to perform real-world tasks and follow human instructions. Its practical success depends on the model learning a broader set of instructions than those it was trained on. Yet the factors that determine model generalization to such \emph{unseen tasks} are not well understood. %To understand the driving factors of generalization, In this paper, we experiment with string rewrites, a symbolic task that serves as a building block for Turing complete Markov algorithms while allowing experimental control of "inputs" and "instructions". We investigate the trade-off between the number of instructions the model is trained on and the number of training samples provided for each instruction and observe that the diversity of the instruction set determines generalization. Generalization emerges once a diverse enough set of tasks is provided, even though very few examples are provided for each task. Instruction diversity also ensures robustness with respect to non-uniform distributions of instructions in the training set.
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