@inproceedings{zhu-etal-2025-tag,
title = "Tag-Instruct: Controlled Instruction Complexity Enhancement through Structure-based Augmentation",
author = "Zhu, He and
Ruan, Zhiwen and
Su, Junyou and
He, Xingwei and
Chen, Yun and
Zhang, Wenjia and
Chen, Guanhua",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.911/",
doi = "10.18653/v1/2025.findings-acl.911",
pages = "17708--17729",
ISBN = "979-8-89176-256-5",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Tag-Instruct: Controlled Instruction Complexity Enhancement through Structure-based Augmentation
%A Zhu, He
%A Ruan, Zhiwen
%A Su, Junyou
%A He, Xingwei
%A Chen, Yun
%A Zhang, Wenjia
%A Chen, Guanhua
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F zhu-etal-2025-tag
%X 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.
%R 10.18653/v1/2025.findings-acl.911
%U https://aclanthology.org/2025.findings-acl.911/
%U https://doi.org/10.18653/v1/2025.findings-acl.911
%P 17708-17729
Markdown (Informal)
[Tag-Instruct: Controlled Instruction Complexity Enhancement through Structure-based Augmentation](https://aclanthology.org/2025.findings-acl.911/) (Zhu et al., Findings 2025)
ACL