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IEEE/ICACT20240321 Slide.03        [Big Slide]	Oral Presentation
	Where traditional deep learning models learn a function f(x) mapping inputs to outputs, LLMs are prompted to exploit their knowledge capabilities, formalized as f(prompt, x). Prompting strategies for LLMs fall into several categories: Zero-shot prompting - No demonstration data, only a prompt string One-shot prompting - A single demonstrative input-output example Few-shot prompting - A small set of examples, like 5-10 Role prompting - Framing the LLM's output role via the prompt Chain-of-thought prompting - Multi-step prompting that traces model reasoning Prompting unlocks the knowledge already encoded in LLMs' parameters without further training. Strategies range from providing no data to few-shot demonstration, and prompting the model role. Chain-of-thought prompting traces step-by-step inference. Together these methods allow LLMs to perform diverse reasoning-intensive tasks.

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