Neuron-Aware Data Selection In Instruction Tuning For Large Language Models
Instruction Tuning (IT) has been proven to be an effective approach to unlock the powerful capabilities of large language models (LLMs). Recent studies indicate that excessive IT data can degrade LLMs performance, while carefully selecting a small subset of high-quality IT data can significantly enhance their capabilities. Therefore, identifying the most efficient subset data from the IT dataset to effectively develop either specific or general abilities in LLMs has become a critical challenge. To address this, we propose a novel and efficient framework called NAIT. NAIT evaluates the impact of IT data on LLMs performance by analyzing the similarity of neuron activation patterns between t...
| Authors | Xin Chen, Junchao Wu, Shu Yang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 13 March 2026 |
| Last updated at source | 13 March 2026 |
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