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MulDimIF: A Multi-Dimensional Constraint Framework for Evaluating and Improving Instruction Following in Large Language Models

Paper · 2026 · Junjie Ye, Caishuang Huang, Zhuohan Chen et al.

Summary

Most instruction-following benchmarks for LLMs vary only the type of constraint. They say little about how constraints are phrased or how hard their combinations are, and they offer little insight into how to improve models. MulDimIF addresses this with a framework that describes each instruction along three axes: the pattern used to present constraints, the constraint category, and a difficulty level based on how many constraints are combined. The authors build a controllable pipeline that adds constraints step by step, removes instructions with conflicting or redundant requirements, and rewrites the rest into each pattern. The result is 9,106 samples whose compliance can be checked automatically with code.

They tested 18 LLMs and found that accuracy drops sharply as constraints accumulate. Training models with GRPO on the generated data improved instruction following while general abilities stayed intact. Further analysis traces most of the gain to changes in attention parameters. This matters for agentic and tool-use settings, where strict output formats such as JSON must be respected.

Key points

  • Framework structure:
    • Three constraint patterns: example (few-shot), listing (numbered rules), and incorporation (constraints woven into the prompt).
    • Four categories: content, language, format, and length, split into 13 subcategories.
    • Four difficulty levels.
  • Generation pipeline: constraint expansion, then conflict detection, then instruction rewriting, producing 9,106 code-verifiable instances.
  • Evaluation of 18 LLMs from six families:
    • Average accuracy is 80.82% for Level I instructions but declines to 36.76% by Level IV.
    • The strongest model reaches only 67.50% overall.
    • Models handle the example pattern far better than the listing or incorporation patterns.
  • Training with GRPO on MulDimIF data: instruction following improves notably, and scores on general benchmarks remain stable.
  • Analysis of the gains:
    • Parameter-level analysis credits most of the improvement to updates in attention modules.
    • Token-importance visualizations show more weight on constraint-related tokens after training.
    • Code and data are released at github.com/Junjie-Ye/MulDimIF.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Fudan UniversityShanghai · China167 works · 20,987 citations🏢TencentShenzhen · China134 works · 19,743 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 3,320 works

Cite

Cite as Junjie Ye, Caishuang Huang, Zhuohan Chen, Wenjie Fu, Chenyuan Yang, Leyi Yang, Yilong Wu, Peng Wang, Meng Zhou, Xiaolong Yang, Tao Gui, Qi Zhang, Zhongchao Shi, Jianping Fan and Xuanjing Huang. 2026. MulDimIF: A Multi-Dimensional Constraint Framework for Evaluating and Improving Instruction Following in Large Language Models. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-acl.99

@inproceedings{ye2026muldimif,
  title = {{MulDimIF: A Multi-Dimensional Constraint Framework for Evaluating and Improving Instruction Following in Large Language Models}},
  author = {Junjie Ye, Caishuang Huang, Zhuohan Chen, Wenjie Fu, Chenyuan Yang, Leyi Yang, Yilong Wu, Peng Wang, Meng Zhou, Xiaolong Yang, Tao Gui, Qi Zhang, Zhongchao Shi, Jianping Fan and Xuanjing Huang},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.99},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.99}
}

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