  With contributions from the open-source community, a vast amount of
instruction tuning (IT) data has emerged. Given the significant resource
allocation required by training and evaluating models, it is advantageous to
have an efficient method for selecting high-quality IT data. However, existing
methods for instruction data selection have limitations such as relying on
fragile external APIs, being affected by biases in GPT models, or reducing the
diversity of the selected instruction dataset. In this paper, we propose an
industrial-friendly, expert-aligned and diversity-preserved instruction data
selection method: Clustering and Ranking (CaR). CaR consists of two steps. The
first step involves ranking instruction pairs using a scoring model that is
well aligned with expert preferences (achieving an accuracy of 84.25%). The
second step involves preserving dataset diversity through a clustering
process.In our experiment, CaR selected a subset containing only 1.96% of
Alpaca's IT data, yet the underlying AlpaCaR model trained on this subset
outperforms Alpaca by an average of 32.1% in GPT-4 evaluations. Furthermore,
our method utilizes small models (355M parameters) and requires only 11.2% of
the monetary cost compared to existing methods, making it easily deployable in
industrial scenarios.
