Prompt Based Method

Prompt-based methods leverage the capabilities of large language models (LLMs) by carefully crafting input prompts to guide model behavior and improve performance on various tasks, including text generation, question answering, and classification. Current research focuses on enhancing prompt design for improved efficiency and robustness, exploring techniques like reparameterization, multi-prompt evaluation, and consistent prompting across training and testing phases. These methods offer parameter-efficient fine-tuning alternatives, enabling cost-effective adaptation of LLMs to specific applications while addressing challenges like prompt sensitivity and adversarial attacks, thereby significantly impacting both the efficiency and reliability of LLM-based systems.