Stanford & CZ Biohub's TEXTGRAD: Transforming AI Optimization with Textual Feedback | Synced
Briefly

TEXTGRAD enables automatic differentiation via text, utilizing LLMs to provide natural language feedback for optimizing variables in computation graphs across various domains.
The framework is versatile, user-friendly, and open-source, focusing on optimizing complex functions with interpretable textual gradients for better system performance.
Read at Synced | AI Technology & Industry Review
[
|
]