The emergence of large language models (LLMs) has revolutionized problem-solving approaches. In the past, tasks like document reformatting or sentence classification necessitated creating specific computer programs. LLMs have transformed this process, enabling tasks to be accomplished through textual prompts. For instance, reformatting documents can be achieved by instructing an LLM. This shift was exemplified by GPT-3’s ability to achieve accurate results with minimal guidance.
As LLM research progressed, more sophisticated techniques emerged beyond basic prompting methods like zero/few-shot learning. Instruction-following LLMs (e.g., InstructGPT, ChatGPT) prompted investigations into tackling complex tasks. The goal was to extend LLMs beyond simple problems, requiring them to comprehend intricate instructions and execute multi-step reasoning. However, such challenges demand advanced prompting strategies due to their complexity.