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    <abstract>Recent studies show LLMs struggle with complex instructions involving multiple constraints (e.g., length, format, sentiment). Existing research enhances open-source LLMs using closed-source guidance (e.g., GPT-4), but this heavily relies on generated data quality. An alternative is leveraging LLMs’ self-correction to refine responses for better constraint adherence. However, this is limited by the feedback quality, as we found LLMs cannot generate reliable feedback or detect errors. Moreover, the self-correction effectiveness relies on few-shot examples illustrating response modifications. As constraints in complex instructions are diverse, manually crafting such examples for each constraint type can be labor-intensive and sub-optimal. To address these two challenges, we propose the Divide-Verify-Refine (DVR) framework with three steps: (1) Divide complex instructions into single constraints and prepare appropriate tools; (2) Verify responses using tools that provide rigorous check and textual guidance (e.g., Python scripts for format checks or pre-trained classifiers for content analysis); (3) Refine: To maximize refinement effectiveness, we propose dynamic few-shot prompting, where a refinement repository collects successful refinements, and these examples are selectively retrieved for future refinements. Recognizing the lack of complexity in existing datasets, we create a new dataset of complex instructions. DVR doubles Llama3.1-8B’s constraint adherence and triples Mistral-7B’s performance.</abstract>
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%0 Conference Proceedings
%T Divide-Verify-Refine: Can LLMs Self-align with Complex Instructions?
%A Zhang, Xianren
%A Tang, Xianfeng
%A Liu, Hui
%A Wu, Zongyu
%A He, Qi
%A Lee, Dongwon
%A Wang, Suhang
%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 zhang-etal-2025-divide
%X Recent studies show LLMs struggle with complex instructions involving multiple constraints (e.g., length, format, sentiment). Existing research enhances open-source LLMs using closed-source guidance (e.g., GPT-4), but this heavily relies on generated data quality. An alternative is leveraging LLMs’ self-correction to refine responses for better constraint adherence. However, this is limited by the feedback quality, as we found LLMs cannot generate reliable feedback or detect errors. Moreover, the self-correction effectiveness relies on few-shot examples illustrating response modifications. As constraints in complex instructions are diverse, manually crafting such examples for each constraint type can be labor-intensive and sub-optimal. To address these two challenges, we propose the Divide-Verify-Refine (DVR) framework with three steps: (1) Divide complex instructions into single constraints and prepare appropriate tools; (2) Verify responses using tools that provide rigorous check and textual guidance (e.g., Python scripts for format checks or pre-trained classifiers for content analysis); (3) Refine: To maximize refinement effectiveness, we propose dynamic few-shot prompting, where a refinement repository collects successful refinements, and these examples are selectively retrieved for future refinements. Recognizing the lack of complexity in existing datasets, we create a new dataset of complex instructions. DVR doubles Llama3.1-8B’s constraint adherence and triples Mistral-7B’s performance.
%R 10.18653/v1/2025.findings-acl.709
%U https://aclanthology.org/2025.findings-acl.709/
%U https://doi.org/10.18653/v1/2025.findings-acl.709
%P 13783-13800

Markdown (Informal)

[Divide-Verify-Refine: Can LLMs Self-align with Complex Instructions?](https://aclanthology.org/2025.findings-acl.709/) (Zhang et al., Findings 2025)

• Divide-Verify-Refine: Can LLMs Self-align with Complex Instructions? (Zhang et al., Findings 2025)

ACL

• Xianren Zhang, Xianfeng Tang, Hui Liu, Zongyu Wu, Qi He, Dongwon Lee, and Suhang Wang. 2025. Divide-Verify-Refine: Can LLMs Self-align with Complex Instructions?. In Findings of the Association for Computational Linguistics: ACL 2025, pages 13783–13800, Vienna, Austria. Association for Computational Linguistics.