Researchers at Stanford University, including M Yuksekgonul, F Bianchi, J Boen, S Liu, and Z Huang, have introduced TEXTGRAD, a novel framework designed to optimize AI systems by backpropagating textual feedback provided by large language models (LLMs). This innovative approach, described as 'automatic differentiation via text,' has demonstrated significant improvements in performance metrics, achieving a 51% to 55% increase on GPQA and a 20% relative gain in LeetCode-Hard. TEXTGRAD, which has been likened to PyTorch-for-text, is poised to enhance various applications, including designing new molecules and improving medical treatments.
Researchers at Stanford Introduce TEXTGRAD: A Powerful AI Framework Performing Automatic “Differentiation” via Text https://t.co/FK6at2OnL1 #AI #AIframework #AIautomation #AIsolutions #Textgrad #ai #news #llm #ml #research #ainews #innovation #artificialintelligence #machinel… https://t.co/H0HkV9ly6r
With LLMs becoming a common tool today, we've innovated a way to enhance responses by introducing automated "differentiation" via text. Super excited to be part of this project to build PyTorch-for-text!🔥 https://t.co/k7fYsjwhno
⚡️This is the most fun project! We built PyTorch-for-text! 🔥 #TextGrad: automated "differentiation" via text to optimize AI systems by backpropagating LLM text feedback. TextGrad + GPT4o: 💻LeetCodeHard best score ❓GPQA sota 🧬Designs new molecules 🩺Improves treatments 🧵 https://t.co/eFLqVM4VH9
[CL] TextGrad: Automatic "Differentiation" via Text M Yuksekgonul, F Bianchi, J Boen, S Liu, Z Huang… [Stanford University] (2024) https://t.co/kwoNwi7SOz - This paper introduces TEXTGRAD, a framework for optimizing AI systems by backpropagating textual feedback provided by… https://t.co/Aeuon9Eo9A
TextGrad Automatic "Differentiation" via Text AI is undergoing a paradigm shift, with breakthroughs achieved by systems orchestrating multiple large language models (LLMs) and other complex components. As a result, developing principled and automated optimization https://t.co/PIhUmVtHSW
TextGrad: Automatic "Differentiation" via Text - Backprops textual feedback provided by LLMs to improve individual components of a compound AI system - 51% -> 55% on GPQA and 20% rel. gain in LeetCode-Hard repo: https://t.co/HtAj2xOo8J abs: https://t.co/ijttO7dv0Y https://t.co/sdZMrmsUxK
Cool new work combining large language models and POET. cc @joelbot3000 @kenneth0stanley @ruiwang2uiuc @AdityaRawaI https://t.co/mVRa5LaFsM