AlpaCream: an Effective Method of Data Selection on Alpaca

Instruction Fine-Tuning (IFT) optimizes Large Language Models (LLMs) to enhance the comprehension and execution of user instructions through extensive instruction datasets. However, these datasets are often voluminous, repetitive, and contain a substantial proportion of low-quality data, necessitating effective selection. Traditional data selection methods are plagued by challenges such as insufficient diver-sity, unclear selection criteria, biases from external LLMs, and excessive resource consumption. This paper proposes AlpaCream, a novel instruction data selection methodology designed for industrial applications that aligns with expert insights and ensures maximum diversity of the data. Our approach involves initially categorizing the instruction data into dense clusters using a topic model and then employing a quality assessment model to isolate high-quality instruction data subsets based on their categorizations. The selected data is further improved by using prompt to enhance the data quality. Finally, the augmented data are used to fine-tune the base LLM to get a model with instruction-following solid capability. It can achieve an average performance increase of 31.3% to 39.8% on the Alpaca_52k dataset, which uses 5.76% of the total instruction data. In general, AlpaCream achieved superior performance over comparable instruction data selection methods.