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

2025-08-23

AI Summary

To address the critical misalignment between large language models (LLMs) and human intent, safety constraints, and domain-specific requirements, this paper proposes an alignment-centric instruction tuning paradigm. Methodologically, it systematically integrates three core components: (1) data construction—encompassing expert annotation, model distillation, and self-improvement; (2) efficient fine-tuning—including full-parameter tuning, LoRA, and prefix tuning; and (3) multidimensional evaluation—featuring automated generation, adaptive optimization, and robustness validation. It innovatively classifies and unifies data construction strategies and establishes a multilingual, multimodal, domain-specific benchmark covering healthcare, law, finance, and other high-stakes fields. The key contribution is the first reusable technical framework and practical guideline that jointly optimizes alignment depth, training efficiency, and evaluation reliability—demonstrably enhancing LLMs’ safety, reliability, and domain adaptability in complex real-world scenarios.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Safety and Robustness
• Computer Vision: Large Vision Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

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.

Problem

• Surveying instruction tuning techniques for aligning LLMs with human intentions
• Examining data collection, fine-tuning strategies, and evaluation protocols
• Addressing challenges in multilingual, multimodal, and domain-specific applications

Innovation

• Instruction tuning for human alignment
• Data construction with expert annotation
• Parameter-efficient fine-tuning strategies