DeepMind Discovers Simple Ways to Enhance Language Model Inference Capabilities

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Published inAI News · 1 min read · Feb 26, 2024

Translated Data: DeepMind's latest research reveals that language models still face challenges in logical reasoning. Studies show that the order of premises in a task significantly impacts the logical reasoning performance of language models. This finding could guide experts in making decisions when using language models for basic reasoning tasks. Altering the order of premises might be a simple and effective way to enhance the reasoning capabilities of language models.

Jieyue Xingchen and Zhiyuan Robotics Partner to Explore Large Model + Embodied Robot Applications

Shanghai Jieyue Xingchen Intelligent Technology Co., Ltd. and Zhiyuan Robotics have officially signed a deep strategic cooperation agreement. Both parties will conduct in-depth cooperation in the fields of base large models and robot research and development, jointly exploring technological breakthroughs and application innovations of "large model + embodied robot." This cooperation involves world model technology research and development, data cooperation in the field of embodied intelligence, and the implementation of application scenarios such as new retail, aiming to promote the large-scale application of embodied intelligence technology in areas such as home services, new retail, and intelligent manufacturing.

Survey: 52% of US Adults Have Used AI Chatbots

A survey by Elon University shows that 52% of US adults have used AI large language models like ChatGPT, Gemini, Claude, and Copilot. The January survey, conducted by the Imagining the Digital Future initiative at Elon University in North Carolina, polled 500 respondents. Of those who have used AI, 34% reported using large language models at least once a day. ChatGPT was the most popular, used by 72% of respondents; Google's G...

Million-Dollar LLM Training Unveils Step Law: Jieyue Xingchen Releases Universal Hyperparameter Optimization Tool

In the fiercely competitive AI landscape, a million-dollar experiment is quietly revolutionizing large language model (LLM) training. Jieyue Xingchen's research team recently released groundbreaking findings. By utilizing nearly 1 million NVIDIA H800 GPU hours, they trained 3,700 models of varying sizes from scratch, accumulating a staggering 100 trillion tokens. This revealed a universal scaling law dubbed 'Step Law,' offering a novel guide for efficient LLM training.

CMU Team Introduces Meta Reinforcement Fine-Tuning: A Novel Paradigm for Enhancing Large Language Model Reasoning

Large Language Models (LLMs) are constantly evolving in the field of artificial intelligence. Researchers from Carnegie Mellon University (CMU) and HuggingFace recently introduced a new method called Meta Reinforcement Fine-Tuning (MRT). This method aims to optimize the computational efficiency of LLMs during testing, particularly excelling in solving complex reasoning problems. Studies show that existing LLMs struggle with...