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Instruction tuning for large language models (LLMs) has gained attention fromresearchers due to its ability to unlock the potential of LLMs in followinginstructions. While instruction tuning offers advantages for facilitating theadaptation of large language models (LLMs) to downstream tasks as a fine-tuningapproach, training models with tens of millions or even billions of parameterson large amounts of data results in unaffordable computational costs. Toaddress this, we focus on reducing the data used in LLM instruction tuning todecrease training costs and improve data efficiency, dubbed as Low TrainingData Instruction Tuning (LTD Instruction Tuning). Specifically, this paperconducts a preliminary exploration into reducing the data used in LLM trainingand identifies several observations regarding task specialization for LLMtraining, such as the optimization of performance for a specific task, thenumber of instruction types required for instruction tuning, and the amount ofdata required for task-specific models. The results suggest that task-specificmodels 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.

Maybe Only 0.5% Data is Needed: A Preliminary Exploration of Low Training Data Instruction Tuning