arxiv Preprint - Automatic Prompt Optimization with ”Gradient Descent” and Beam Search

In this episode we discuss Automatic Prompt Optimization with "Gradient Descent" and Beam Search by Reid Pryzant, Dan Iter, Jerry Li, Yin Tat Lee, Chenguang Zhu, Michael Zeng. The paper introduces ProTeGi, a method for improving prompts used in large language models. It utilizes mini-batches of data to generate "natural language gradients" that provide feedback on the prompt. ProTeGi uses beam search and bandit selection to efficiently modify the prompt, resulting in improved performance on benchmark NLP tasks and a novel LLM jailbreak detection problem. This method reduces manual effort and enhances task performance by automatically optimizing prompts.