Work

Tag-Instruct: Controlled Instruction Complexity Enhancement through Structure-based Augmentation

Year: 2025

Type: conference-paper

Abstract: High-quality instruction data is crucial for developing large language models (LLMs), yet existing approaches struggle to effectively control instruction complexity.We present TAG-INSTRUCT, a novel framework that enhances instruction complexity through structured semantic compression and controlled difficulty augmentation.Unlike previous prompt-based methods operating on raw text, TAG-INSTRUCT compresses instructions into a compact tag space and systematically enhances complexity through RL-guided tag expansion.Through extensive experiments, we show that TAG-INSTRUCT outperforms existing instruction complexity augmentation approaches.Our analysis reveals that operating in tag space provides superior controllability and stability across different instruction synthesis frameworks.

DOI: 10.18653/v1/2025.findings-acl.911

Authors: He Zhu, Zhiwen Ruan, Junyou Su, Xingwei He, Yun Chen, Wenjia Zhang, Guanhua Chen

Institutions: Peking University, Southern University of Science and Technology, University of Hong Kong, Shanghai University of Finance and Economics, Tongji University

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Funders: National Natural Science Foundation of China, Shanghai Municipal Education Commission, Southern University of Science and Technology, Basic and Applied Basic Research Foundation of Guangdong Province

OpenAlex ID: W4412888036

FWCI: 0.991

Cites:

Cited by: 1

Related to: 10