MuSC: Improving Complex Instruction Following with Multi-granularity Self-Contrastive Training

• URL: http://arxiv.org/abs/2502.11541v2
• Date: Sun, 23 Feb 2025 05:56:44 GMT
• Title: MuSC: Improving Complex Instruction Following with Multi-granularity Self-Contrastive Training
• Authors: Hui Huang, Jiaheng Liu, Yancheng He, Shilong Li, Bing Xu, Conghui Zhu, Muyun Yang, Tiejun Zhao,
• Abstract summary: We propose a Multi-granularity Self-Contrastive Training (MuSC) framework to improve the complex instruction alignment without relying on a stronger model.<n>Our method is evaluated on open-sourced models, and experiment results show our method achieves significant improvement on both complex and general instruction-following benchmarks.
• Score: 36.483136685734735
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Complex instruction-following with elaborate constraints is imperative for Large Language Models (LLMs). While existing methods have constructed data for complex instruction alignment, they all rely on a more advanced model, especially GPT-4, limiting their application. In this paper, we propose a Multi-granularity Self-Contrastive Training (MuSC) framework, to improve the complex instruction alignment without relying on a stronger model. Our method is conducted on both coarse and fine granularity. On coarse-granularity, we construct constraint-aware preference data based on instruction decomposition and recombination. On fine-granularity, we perform token-aware preference optimization with dynamic token-level supervision. Our method is evaluated on open-sourced models, and experiment results show our method achieves significant improvement on both complex and general instruction-following benchmarks, surpassing previous self-alignment methods.

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