Neuron-Aware Data Selection In Instruction Tuning For Large Language Models

Abstract

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 the IT dataset and the target domain capability. Specifically, NAIT captures neuron activation patterns from in-domain datasets of target domain capabilities to construct reusable and transferable neuron activation features. It then evaluates and selects optimal samples based on the similarity between candidate samples and the expected activation features of the target capabilities. Experimental results show that training on the 10\% Alpaca-GPT4 IT data subset selected by NAIT consistently outperforms methods that rely on external advanced models or uncertainty-based features across various tasks. Our findings also reveal the transferability of neuron activation features across different capabilities of LLMs. In particular, IT data with more logical reasoning and programmatic features possesses strong general transferability, enabling models to develop stronger capabilities across multiple tasks, while a stable core subset of data is sufficient to consistently activate fundamental model capabilities and universally improve performance across diverse tasks.

Figures (7)

• Figure 1: Overall framework of Nait. First, we capture the neuron activation of the LLM using in-domain data that we want the model to learn. Next, we construct an activation feature through a dimensionality reduction method. Finally, we evaluate the feature alignment score between this activation feature and the model's activation on each candidate dataset to guide data selection.
• Figure 2: Performance at different proportions of the IT dataset. Task performance in Nait (the in-domain dataset), e.g., MMLU in Nait, refers to using in-domain data to guide IT data selection and to assess task outcomes. The comprehensive results correspond to System 12.
• Figure 3: Comparison of Nait with random data selection across different in-domain data scales at 30% IT dataset. Task performance in Nait (in-domain dataset), such as MMLU in Nait (MMLU), refers to using the in-domain dataset to guide IT data selection and evaluate task performance. The dashed line indicates the 30% of the IT dataset random selection baseline.
• Figure 4: Comparison of Nait across different IT dataset. Task performance in Nait refers to using the all in-domain dataset to guide IT data selection and evaluate task performance.
• Figure 5: Transferability of neural activation features across capabilities. Each column represents the capability to which the neural activation feature is applied. Icons with an outline border indicate the capability’s performance on its own task, which serves as the baseline reference.