From Complexity to Simplicity: Advancements in Large Language Model Compression

The rapid evolution of large language models (LLMs) has brought transformative advancements to natural language processing (NLP), enabling unprecedented performance in tasks such as machine translation, text generation, and conversational AI. However, the immense computational demands, memory requirements, and energy consumption of these models pose significant challenges for real-world deployment, particularly on resource-constrained devices and systems. Knowledge distillation has emerged as a pivotal technique for addressing these challenges, providing a framework for transferring knowledge from large, complex teacher models to smaller, efficient student models.This survey presents a comprehensive review of knowledge distillation methods tailored to LLMs, highlighting key advancements, applications, and unresolved challenges. We explore traditional distillation strategies, including logit matching and feature alignment, alongside contemporary approaches such as task-specific adaptations, attention map transfer, and progressive layer-by-layer distillation. Additionally, the integration of knowledge distillation with complementary compression techniques, such as quantization, pruning, and low-rank factorization, is examined, demonstrating their synergistic potential in optimizing LLMs for practical use.Applications of knowledge distillation span diverse domains, including edge computing, real-time systems, and fine-tuning for specialized tasks. The technique has facilitated the democratization of LLMs, enabling accessibility for organizations with limited computational resources. Despite these successes, challenges such as the efficient distillation of emergent behaviors, generalization in low-resource domains, and scalability for ultra-large teacher models remain significant barriers. Furthermore, the environmental impact and ethical considerations associated with LLM compression and deployment underscore the need for responsible innovation.Future directions in knowledge distillation research include the development of dynamic and adaptive distillation frameworks, automated processes leveraging neural architecture search, and benchmarks tailored to evaluate distillation outcomes comprehensively. By addressing these challenges, knowledge distillation can further enhance the efficiency, scalability, and inclusivity of LLMs, shaping the next generation of NLP systems.