Current pre-trained large language models typically need instruction tuning to align with human preferences. However, instruction tuning data is often quantity-saturated due to the large volume of data collection and fast model iteration, leaving coreset data selection important but underexplored. On the other hand, existing quality-driven data selection methods such as LIMA (NeurIPS 2023 (Zhou et al., 2024)) and AlpaGasus (ICLR 2024 (Chen et al.)) generally ignore the equal importance of data diversity and complexity. In this work, we aim to design a diversity-aware data selection strategy and creatively propose using sparse autoencoders to tackle the challenge of data diversity measure. In addition, sparse autoencoders can also provide more interpretability of model behavior and explain, e.g., the surprising effectiveness of selecting the longest response (ICML 2024 (Zhao et al.)). Using effective data selection, we experimentally prove that models trained on our selected data can outperform other methods in terms of model capabilities, reduce training cost, and potentially gain more control over model behaviors.

本研究针对当前预训练大型语言模型在与人类偏好对齐时面临的数据选择问题，提出了一种关注数据多样性的数据选择策略。通过使用稀疏自编码器来衡量数据多样性，本研究不仅提供了更好的模型可解释性，还实验证明了在精选数据上训练的模型在能力上超越其他方法，降低了训练成本，并可能更好地控制模型行为。

基于多样性驱动的数据选择用于语言模型调优的稀疏自编码器