Prompting Based

Prompting-based techniques are revolutionizing how we interact with and improve large language models (LLMs), focusing on crafting effective input prompts to elicit desired outputs and behaviors. Current research explores diverse prompting strategies, including chain-of-thought prompting, few-shot learning, and various prompt engineering methods, often applied to models like GPT-3, LLaMA, and Gemini. This approach is significantly impacting fields like question answering, text generation, and even adversarial robustness, offering efficient alternatives to extensive model retraining and enabling more nuanced control over LLM capabilities.