Towards Alignment-Centric Paradigm: A Survey of Instruction Tuning in Large Language Models
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arXiv
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| Format: | Preprint |
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2025
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| author | Han, Xudong Yang, Junjie Wang, Tianyang Bi, Ziqian Song, Xinyuan Hao, Junfeng Song, Junhao |
| author_facet | Han, Xudong Yang, Junjie Wang, Tianyang Bi, Ziqian Song, Xinyuan Hao, Junfeng Song, Junhao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_17184 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Alignment-Centric Paradigm: A Survey of Instruction Tuning in Large Language Models Han, Xudong Yang, Junjie Wang, Tianyang Bi, Ziqian Song, Xinyuan Hao, Junfeng Song, Junhao Computation and Language I.2.7; I.2.6 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. |
| title | Towards Alignment-Centric Paradigm: A Survey of Instruction Tuning in Large Language Models |
| topic | Computation and Language I.2.7; I.2.6 |
| url | https://arxiv.org/abs/2508.17184 |