Instruction tuning for large language models (LLMs) has gained attention from researchers due to its ability to unlock the potential of LLMs in following instructions. While instruction tuning offers advantages for facilitating the adaptation of large language models (LLMs) to downstream tasks as a fine-tuning approach, training models with tens of millions or even billions of parameters on large amounts of data results in unaffordable computational costs. To address this, we focus on reducing the data used in LLM instruction tuning to decrease training costs and improve data efficiency, dubbed as Low Training Data Instruction Tuning (LTD Instruction Tuning). Specifically, this paper conducts a preliminary exploration into reducing the data used in LLM training and identifies several observations regarding task specialization for LLM training, such as the optimization of performance for a specific task, the number of instruction types required for instruction tuning, and the amount of data required for task-specific models. The results suggest that task-specific models can be trained using less than 0.5% of the original dataset, with a 2% improvement in performance over those trained on full task-related data.

翻译：大语言模型（LLMs）的指令微调因其能够释放模型遵循指令的潜力而备受研究者关注。尽管指令微调作为一种微调方法，在促进大语言模型适应下游任务方面具有优势，但使用海量数据训练拥有数千万甚至数十亿参数的模型会导致难以承受的计算成本。为解决这一问题，我们聚焦于减少LLM指令微调中使用的数据量，以降低训练成本并提升数据效率，称之为低训练数据指令微调（LTD Instruction Tuning）。具体而言，本文对减少LLM训练数据量进行了初步探索，并总结出若干关于LLM训练任务专业化的观测结果，包括：特定任务性能的优化、指令微调所需指令类型的数量，以及任务专用模型所需的数据量。结果表明，使用原始数据集的不到0.5%即可训练任务专用模型，且其性能较之基于完整任务相关数据训练的模型提升了2%。