Prompt Based Knowledge

Prompt-based knowledge leverages the ability of large language models (LLMs) to access and utilize knowledge implicitly encoded within their parameters by strategically crafting input prompts. Current research focuses on improving the accuracy, consistency, and reliability of knowledge retrieval through techniques like hypothesis testing prompting and knowledge pursuit prompting, which incorporate external knowledge sources to refine prompts and enhance model reasoning. This approach is proving valuable across diverse applications, including multimodal generation, question answering, and medical information extraction, by enabling more effective knowledge integration and reducing reliance on extensive fine-tuning. The ultimate goal is to create more robust and reliable LLMs that can access and utilize knowledge effectively for a wide range of tasks.