AI Technology & Industry Review
In the new paper Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models, a Microsoft Research Asia team presents Visual ChatGPT, a system that incorporates various visual foundation models to enable ChatGPT to understand, generate and edit visual information.
In the new paper Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback, a Microsoft Research and Columbia University team presents LLM-Augmenter, a system that augments black-box large language models with a set of plug-and-play modules to significantly improve the factuality of their responses.
Colossal-AI, as one of the hottest open-source solutions for large AI models, presents an open-source complete PyTorch-based ChatGPT equivalent implementation process that achieves 7.73 times faster compared to the original PyTorch approach with only 1.6GB GPU memory.
In the new paper Mathematical Capabilities of ChatGPT, an international research team tests ChatGPT’s mathematical capabilities and evaluates its suitability as an assistant to professional mathematicians. The team concludes that despite the glowing reviews in mainstream media, ChatGPT’s mathematical abilities “are significantly below those of an average mathematics graduate student.”
In the new paper DetectGPT: Zero-Shot Machine-Generated Text Detection Using Probability Curvature, a Stanford University research team presents DetectGPT, a zero-shot machine-generated text detection algorithm that uses probability curvature to predict whether a candidate passage was generated by a large language model.