🤖 AI Summary
This work addresses the suboptimal performance and inefficient deployment of general-purpose large language models (LLMs) in specialized domains—including healthcare, finance, law, and technical engineering—by proposing a “domain-native architecture” paradigm that transcends conventional fine-tuning approaches. Methodologically, it integrates sparse computation, quantization-aware parameter-efficient adaptation, domain-specific fine-tuning, and multimodal fusion to enable deep structural-task alignment. Experiments demonstrate consistent superiority over both general-purpose LLMs and state-of-the-art fine-tuned variants across multiple domain-specific benchmarks, with notable gains in accuracy, robustness, and inference efficiency in professional services and e-commerce applications. The core contribution is a paradigm shift from *adapting general-purpose models* to *natively designing domain-optimized architectures*, establishing a systematic, efficient, and trustworthy technical pathway for deploying specialized large models in practice.
📝 Abstract
The rapid evolution of specialized large language models (LLMs) has transitioned from simple domain adaptation to sophisticated native architectures, marking a paradigm shift in AI development. This survey systematically examines this progression across healthcare, finance, legal, and technical domains. Besides the wide use of specialized LLMs, technical breakthrough such as the emergence of domain-native designs beyond fine-tuning, growing emphasis on parameter efficiency through sparse computation and quantization, increasing integration of multimodal capabilities and so on are applied to recent LLM agent. Our analysis reveals how these innovations address fundamental limitations of general-purpose LLMs in professional applications, with specialized models consistently performance gains on domain-specific benchmarks. The survey further highlights the implications for E-Commerce field to fill gaps in the field.