Stanford & CZ Biohub’s TEXTGRAD: Transforming AI Optimization with Textual Feedback | Synced
In a new paper TextGrad: Automatic ‘Differentiation’ via Text, a research team from Stanford University and CZ Biohub introduces TEXTGRAD, a robust framework that performs automatic dif...
Source: Synced | AI Technology & Industry Review
In a new paper TextGrad: Automatic ‘Differentiation’ via Text, a research team from Stanford University and CZ Biohub introduces TEXTGRAD, a robust framework that performs automatic differentiation through text. In this system, LLMs generate comprehensive, natural language suggestions to optimize variables in computation graphs.