LLaMA-Adapter + MRP: Integrating Meta-Reasoning Prompting with LLaMA-Adapter for Efficient Multi-Modal and Task-Adaptive Reasoning

摘要

The rapid adaptation of large language models (LLMs) to diverse tasks and modalities remains hindered by computational inefficiency and rigid reasoning strategies. This paper introduces a novel framework that integrates LLaMA-Adapter, a parameter-efficient fine-tuning method with Meta-Reasoning Prompting (MRP), to address these limitations. LLaMA-Adapter enables the lightweight adaptation of frozen LLMs using only 1.2M trainable parameters (vs. 7B in full fine-tuning), achieving 66% faster convergence while preserving pre-trained knowledge. MRP enhances reasoning adaptability by dynamically selecting optimal strategies (e.g., Chain-of-Thought, Program-of-Thought) based on task context, improving accuracy by 3-12% over static methods. Evaluated across language, reasoning, and multi-modal benchmarks, our framework attains state-of-the-art results: 96.1% on GSM8K (math), 91.2% on MMLU (knowledge), and 89.7% on ScienceQA (vision-language), with 98% fewer parameters than competitive multi-modal baselines. The framework demonstrates robustness under visual perturbations (14% improvement on LVLM-eHub) and a 38% strategy-switch rate, highlighting its contextual awareness. By unifying efficiency and adaptability, this work establishes a new paradigm for scalable AI deployment in resource-constrained environments, with applications in healthcare, education, and robotics.