A Comprehensive Approach to Instruction Tuning for Qwen2.5: Data Selection, Domain Interaction, and Training Protocols
Year: 2025
Type: article
Abstract: Instruction tuning plays a pivotal role in aligning large language models with diverse tasks, yet its effectiveness hinges on the interplay of data quality, domain composition, and training strategies. This study moves beyond qualitative assessment to systematically quantify these factors through extensive experiments on data selection, data mixture, and training protocols. By quantifying performance trade-offs, we demonstrate that the implicit method SuperFiltering achieves an optimal balance, whereas explicit filters can induce capability conflicts. A fine-grained analysis of cross-domain interactions quantifies a near-linear competition between code and math, while showing that tool use data exhibits minimal interference. To mitigate these measured conflicts, we compare multi-task, sequential, and multi-stage training strategies, revealing that multi-stage training significantly reduces Conflict Rates while preserving domain expertise. Our findings culminate in a unified framework for optimizing instruction tuning, offering actionable, data-driven guidelines for balancing multi-domain performance and enhancing model generalization, thus advancing the field by providing a methodology to move from intuition to systematic optimization.
Source: Computers
Authors: Xungang Gu, Mengqi Wang, Yangjie Tian, Ning Li, Jiaze Sun, Jingfang Xu, He Zhang, Ruohua Xu, Ming Liu
Institution: Deakin University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,172
OpenAlex ID: W4412068610