Efficient Optimization of Large Language Models via Parameter-Efficient Tuning and Adaptive Inference

Large Language Models (LLMs) have emerged as a transformative paradigm in artificial intelligence, enabling significant advances in natural language understanding, generation, and reasoning. This paper presents a unified framework for efficient LLM optimization that integrates data-centric learning, parameter-efficient fine-tuning, and adaptive inference strategies. The proposed approach addresses critical challenges in scalability, computational cost, and domain adaptation. Experimental evaluations demonstrate improved performance across multiple benchmarks, highlighting the effectiveness of the framework in balancing accuracy and efficiency.