AI Technology & Industry Review
In a new paper DeepSpeed-Chat: Easy, Fast and Affordable RLHF Training of ChatGPT-like Models at All Scales, a Deepspeed of Microsoft research team presents DeepSpeed-Chat, a novel end-to-end RLHF pipeline that provides easy-to-use training and inference for ChatGPT-like models at scale.
Colossal-AI open sources a complete RLHF pipeline that includes supervised data collection, supervised fine-tuning, reward model training, and reinforcement learning fine-tuning, based on the LLaMA pre-trained model, and shares ColossalChat, the most practical open-source project that closely resembles the original ChatGPT technical solution!
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.