Year: 2025
Type: conference-paper
Abstract: Evol-Instruct has made significant improvements as a data synthesis method in several areas.Existing methods typically rely on a fixed set of strategies to evolve, which require manual design and are monolithic in form.In addition, iterative evolution also makes the acquisition of hard samples expensive.In view of this, we propose the Tag-Evol framework, a more diverse and efficient instruction evolving method.Specifically, Tag-Evol uses diverse and specific knowledge tags as strategies to achieve controlled evolution by injecting different combinations of tags into the original instructions.Experiments with multiple backbones in diverse domain benchmarks show that the proposed method generates significantly better evolved data than other methods.Furthermore, we conduct a thorough analysis of the evolved data, demonstrating that Tag-Evol is not only efficient but also generates more diverse and challenging data.
Authors: Yixuan Wang, Shiqi Zhou, Chuanzhe Guo, Qingfu Zhu
Institution: Harbin Institute of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Awards: G2565984771, G3596954764
OpenAlex ID: W4412888530