arxiv preprint - TextGrad: Automatic ”Differentiation” via Text

In this episode, we discuss TextGrad: Automatic "Differentiation" via Text by Mert Yuksekgonul, Federico Bianchi, Joseph Boen, Sheng Liu, Zhi Huang, Carlos Guestrin, James Zou. The paper introduces TEXTGRAD, a novel framework that automates the optimization of compound AI systems by utilizing textual feedback from large language models (LLMs). TEXTGRAD treats text feedback as a form of "differentiation" to improve the components of these AI systems across various applications, working out-of-the-box without requiring specific tuning. Demonstrating its effectiveness, TEXTGRAD enhances performance in diverse tasks such as question answering, coding problem solutions, molecule design, and treatment planning, marking a significant step forward for the development of advanced AI technologies.