Enhancing Instruction Tuning in LLMs: A Diversity-Aware Data Selection Strategy Using Sparse Autoencoders

Pre-trained LLMs require instruction tuning to align with human preferences. Still, the vast data collection and rapid model iteration often lead to oversaturation, making efficient data selection a crucial yet underexplored area. Existing quality-driven selection methods, such as LIMA and AlpaGasus, tend to overlook the importance of data diversity and complexity, essential for enhancing model […]

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