用稀疏自编码器提升指令微调数据多样性，效果更好且更省训练成本。
Diversity-driven Data Selection for Language Model Tuning through Sparse Autoencoder
- 通过稀疏自编码器量化数据多样性，兼顾复杂度与覆盖范围。
- 在相同训练量下，模型能力超越现有方法，长回复数据更有效。
- 方法可扩展至工业级数据筛选，开源模型供社区复用。
指令微调数据常因数据量大、迭代快而冗余，数据筛选虽关键却研究不足。现有质量导向方法（如LIMA、AlpaGasus）忽略数据多样性和复杂度的同等重要性。本文提出一种面向多样性的数据筛选策略，创新性地使用稀疏自编码器（SAEs）衡量数据多样性，并增强模型行为可解释性，例如揭示选择最长回复为何有效（ICML 2024）。实验表明，使用该方法筛选的数据训练模型，在能力、成本和可控性上均优于现有方法。SAEs可作为多样性度量的有效替代方案，方法具备工业级大规模剪枝的可扩展性，相关训练好的SAEs将对社区开放。
原文摘要 · Abstract (English)
Instruction tuning data are often quantity-saturated due to the large volume of data collection and fast model iteration, leaving data selection important but underexplored. Existing quality-driven data selection methods, such as LIMA (NeurIPS 2023 \citep{zhou2024lima}) and AlpaGasus (ICLR 2024 \citep{chenalpagasus}) 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 (SAEs) to tackle the challenge of data diversity measure. In addition, SAEs can also provide more interpretability of model behavior and explain, e.g., the surprising effectiveness of selecting the longest response (ICML 2024 \citep{zhaolong}). 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. We prove that SAEs can serve as a good alternative to diversity measure and design our method to be scalable for potential industrial large-scale pruning, and we will also release our trained SAEs for use by the broader community.
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