DecIF: Improving Instruction-Following through Meta-Decomposition

• URL: http://arxiv.org/abs/2505.13990v2
• Date: Wed, 11 Jun 2025 03:50:54 GMT
• Title: DecIF: Improving Instruction-Following through Meta-Decomposition
• Authors: Tingfeng Hui, Pengyu Zhu, Bowen Ping, Ling Tang, Guanting Dong, Yaqi Zhang, Sen Su,
• Abstract summary: DecIF is a fully autonomous, meta-decomposition guided framework that generates diverse and high-quality instruction-following data.<n>For instruction generation, we guide LLMs to iteratively produce various types of meta-information, which are then combined with response constraints to form semantically rich instructions.<n>For response generation, we decompose each instruction into atomic-level evaluation criteria, enabling rigorous validation and the elimination of inaccurate instruction-response pairs.
• Score: 9.939860059820917
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Instruction-following has emerged as a crucial capability for large language models (LLMs). However, existing approaches often rely on pre-existing documents or external resources to synthesize instruction-following data, which limits their flexibility and generalizability. In this paper, we introduce DecIF, a fully autonomous, meta-decomposition guided framework that generates diverse and high-quality instruction-following data using only LLMs. DecIF is grounded in the principle of decomposition. For instruction generation, we guide LLMs to iteratively produce various types of meta-information, which are then combined with response constraints to form well-structured and semantically rich instructions. We further utilize LLMs to detect and resolve potential inconsistencies within the generated instructions. Regarding response generation, we decompose each instruction into atomic-level evaluation criteria, enabling rigorous validation and the elimination of inaccurate instruction-response pairs. Extensive experiments across a wide range of scenarios and settings demonstrate DecIF's superior performance on instruction-following tasks. Further analysis highlights its strong flexibility, scalability, and generalizability in automatically synthesizing high-quality instruction data.

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