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.
In a new paper Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffolding, the team introduces meta-prompting. This innovative scaffolding approach proves to be highly effective, surpassing standard prompting by 17.1%, expert (dynamic) prompting by 17.3%, and multi-persona prompting by 15.2%.
In a new paper Prompt2Model: Generating Deployable Models from Natural Language Instructions, a research team from Carnegie Mellon University and Tsinghua University introduces Prompt2Model, a general-purpose approach that is able to use prompting technique to specify system behavior while resulting in a deployable special purpose model that enjoys all the advantages thereof.
In the new paper Point-E: A System for Generating 3D Point Clouds from Complex Prompts, An OpenAI research team presents Point·E, a system for text-conditional synthesis of 3D point clouds that leverages diffusion models to generate diverse and complex 3D shapes conditioned on complex text prompts in minutes on a single GPU.