Structure Guided Prompt

Structure-guided prompting aims to improve the performance of large language models (LLMs) on complex reasoning tasks by carefully crafting prompts that leverage the inherent structure of the input data, often represented as graphs. Current research focuses on developing prompting techniques that enhance LLMs' ability to handle multi-step reasoning, reduce biases, and effectively utilize external tools and knowledge, often employing techniques like chain-of-thought prompting and iterative prompt optimization. This area is significant because improved prompting methods can unlock the full potential of LLMs for various applications, ranging from improved knowledge base creation and fact verification to more robust and ethical text generation.