Neuron-Aware Data Selection In Instruction Tuning For Large Language Models
arXiv:2603.13201v1 Announce Type: new 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
arXiv:2603.13201v1 Announce Type: new 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.
Executive Summary
This article proposes a novel framework called NAIT for neuron-aware data selection in instruction tuning for large language models (LLMs). 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. Experimental results show that training on the 10% Alpaca-GPT4 IT data subset selected by NAIT consistently outperforms other methods across various tasks. The study reveals the transferability of neuron activation features across different capabilities of LLMs, with IT data possessing strong general transferability for logical reasoning and programmatic features. This work has significant implications for the development and fine-tuning of LLMs, and its findings can inform the design of more efficient and effective IT strategies.
Key Points
- ▸ NAIT proposes a novel framework for neuron-aware data selection in IT for LLMs
- ▸ NAIT evaluates the impact of IT data on LLMs performance based on neuron activation patterns
- ▸ The study reveals the transferability of neuron activation features across different LLM capabilities
Merits
Strength in Transferability
The study demonstrates the transferability of neuron activation features across different LLM capabilities, which has significant implications for the development and fine-tuning of LLMs.
Efficient Data Selection
NAIT's framework enables efficient data selection for IT, which can reduce the computational resources required for training and fine-tuning LLMs.
Demerits
Limited Generalizability
The study's findings may not generalize to other LLM architectures or tasks, which could limit the applicability of NAIT's framework.
Dependence on Alpaca-GPT4
The study's results rely on the Alpaca-GPT4 model, which may not be representative of other LLMs or IT datasets.
Expert Commentary
The study proposes a novel and efficient framework for neuron-aware data selection in instruction tuning for LLMs. The framework's evaluation of neuron activation patterns provides a nuanced understanding of how LLMs process and utilize IT data. While the study's findings are promising, the results may not generalize to other LLM architectures or tasks. Furthermore, the study's reliance on the Alpaca-GPT4 model may limit the applicability of NAIT's framework. Nevertheless, the study's contributions to the understanding of LLMs and the development of more efficient and effective IT strategies are significant.
Recommendations
- ✓ Recommendation 1: Further research is needed to investigate the generalizability of NAIT's framework to other LLM architectures and tasks.
- ✓ Recommendation 2: The Alpaca-GPT4 model should be compared to other LLMs to determine the extent to which NAIT's framework is applicable to other models.