A Review of Large Language Models: Fundamental Architectures, Key Technological Evolutions, Interdisciplinary Technologies Integration, Optimization and Compression Techniques, Applications, and Challenges

Analysis

Why This Paper Matters

This survey arrives at a critical juncture where the rapid proliferation of large language models has made it difficult for researchers to maintain a holistic view of the field. By systematically organizing the sprawling literature, the authors provide a much-needed map of LLM research, from foundational architectures to cutting-edge interdisciplinary integrations. The paper's explicit focus on bridging Chinese and English research communities is particularly valuable, as it helps overcome language barriers that often fragment scientific discourse.

The paper's comprehensive scope—covering not only core LLM technologies but also their intersections with contrastive learning, knowledge enhancement, retrieval augmentation, hallucination mitigation, recommendation systems, reinforcement learning, multimodal models, and agents—makes it a one-stop resource for anyone entering the field or seeking to expand their research directions. This breadth is both a strength and a challenge, as it necessarily sacrifices depth in some areas.

Technical Contributions

• Unified Definition: Clarifies the definition of LLMs across different research communities, establishing a common vocabulary.
• Architecture Review: Systematically analyzes mainstream LLM infrastructures, including transformer variants and scaling laws.
• Optimization Taxonomy: Categorizes key optimization and compression techniques such as pruning, quantization, knowledge distillation, and efficient attention mechanisms.
• Interdisciplinary Integration: Provides a detailed review of how LLMs are combined with:
  • Contrastive learning for better representation learning
  • Knowledge enhancement for factual grounding
  • Retrieval augmentation for dynamic knowledge access
  • Hallucination dissolution techniques for reliability
  • Recommendation systems for personalization
  • Reinforcement learning for alignment and task completion
  • Multimodal models for vision-language tasks
  • Agent frameworks for autonomous decision-making
• Deployment Insights: Organizes practical deployment strategies and industry applications, bridging research and practice.

Results

As a review paper, the primary result is a structured synthesis of the current state of LLM research. The paper does not present new experimental metrics but instead catalogs existing findings and identifies promising research directions. It highlights that LLMs have expanded from NLP to computer vision, scientific computing, and vertical industries, demonstrating their transformative potential.

Significance

This survey serves as a foundational reference for AI practitioners and researchers, enabling them to quickly orient themselves in the rapidly evolving LLM landscape. By systematically mapping the integration of LLMs with interdisciplinary technologies, it provides a roadmap for future innovations. The paper's emphasis on practical deployment and industry applications ensures its relevance beyond academia, potentially accelerating the adoption of LLMs in real-world systems. Its identification of current limitations—such as hallucination, computational cost, and alignment challenges—also helps focus future research efforts on the most pressing problems.