Towards Alignment-Centric Paradigm: A Survey of Instruction Tuning in Large Language Models

• URL: http://arxiv.org/abs/2508.17184v1
• Date: Sun, 24 Aug 2025 01:51:55 GMT
• Title: Towards Alignment-Centric Paradigm: A Survey of Instruction Tuning in Large Language Models
• Authors: Xudong Han, Junjie Yang, Tianyang Wang, Ziqian Bi, Junfeng Hao, Junhao Song,
• Abstract summary: This survey provides a comprehensive overview of the full pipeline of instruction tuning strategies.<n>We categorized data construction into three major paradigms: expert annotation, distillation from larger models, and self-improvement mechanisms.<n>We discuss promising directions for automated data generation, adaptive optimization, and robust evaluation frameworks.
• Score: 20.544181414963877
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Instruction tuning is a pivotal technique for aligning large language models (LLMs) with human intentions, safety constraints, and domain-specific requirements. This survey provides a comprehensive overview of the full pipeline, encompassing (i) data collection methodologies, (ii) full-parameter and parameter-efficient fine-tuning strategies, and (iii) evaluation protocols. We categorized data construction into three major paradigms: expert annotation, distillation from larger models, and self-improvement mechanisms, each offering distinct trade-offs between quality, scalability, and resource cost. Fine-tuning techniques range from conventional supervised training to lightweight approaches, such as low-rank adaptation (LoRA) and prefix tuning, with a focus on computational efficiency and model reusability. We further examine the challenges of evaluating faithfulness, utility, and safety across multilingual and multimodal scenarios, highlighting the emergence of domain-specific benchmarks in healthcare, legal, and financial applications. Finally, we discuss promising directions for automated data generation, adaptive optimization, and robust evaluation frameworks, arguing that a closer integration of data, algorithms, and human feedback is essential for advancing instruction-tuned LLMs. This survey aims to serve as a practical reference for researchers and practitioners seeking to design LLMs that are both effective and reliably aligned with human intentions.

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