Optimizing generative AI by backpropagating language model feedback

• 成果类型: Article
• 署名作者: Yuksekgonul, Mert; Bianchi, Federico; Boen, Joseph; Liu, Sheng; Lu, Pan; Huang, Zhi; Guestrin, Carlos; Zou, James
• 署名单位: Stanford University; Stanford University; Chan Zuckerberg Initiative (CZI)
• 刊物名称: Nature
• ISSN/ISSBN: 0028-2364
• DOI: 10.1038/s41586-025-08661-4
• 发表日期: 2025-03-20
• 关键词: algorithms

摘要

Recent breakthroughs in artificial intelligence (AI) are increasingly driven by systems orchestrating multiple large language models (LLMs) and other specialized tools, such as search engines and simulators. So far, these systems are primarily handcrafted by domain experts and tweaked through heuristics rather than being automatically optimized, presenting a substantial challenge to accelerating progress. The development of artificial neural networks faced a similar challenge until backpropagation and automatic differentiation transformed the field by making optimization turnkey. Analogously, here we introduce TextGrad, a versatile framework that performs optimization by backpropagating LLM-generated feedback to improve AI systems. By leveraging natural language feedback to critique and suggest improvements to any part of a system-from prompts to outputs such as molecules or treatment plans-TextGrad enables the automatic optimization of generative AI systems across diverse tasks. We demonstrate TextGrad's generality and effectiveness through studies in solving PhD-level science problems, optimizing plans for radiotherapy treatments, designing molecules with specific properties, coding, and optimizing agentic systems. TextGrad empowers scientists and engineers to easily develop impactful generative AI systems.