In the fast-evolving field of natural language processing, researchers continually seek effective strategies to shrink large language models without sacrificing the core ability to understand meaning, context, and intent. This evergreen guide surveys principled compression techniques, trade-offs, and practical implications for deployment, ensuring that semantic fidelity remains robust under resource constraints, latency requirements, and varied real-world workloads across industries and languages.
