The rise of large language models (LLMs) has sparked questions about their computational abilities compared to traditional models. While recent research has shown that LLMs can simulate a universal ...

Recent large language models (LLMs) have shown impressive performance across a diverse array of tasks. However, their use in high-stakes or computationally constrained environments has highlighted the ...

Large language models (LLMs) like GPTs, developed from extensive datasets, have shown remarkable abilities in understanding language, reasoning, and planning. Yet, for AI to reach its full potential, ...

For artificial intelligence to thrive in a complex, constantly evolving world, it must overcome significant challenges: limited data quality and scale, and a lag in new, relevant information creation.

Multimodal Large Language Models (MLLMs) have rapidly become a focal point in AI research. Closed-source models like GPT-4o, GPT-4V, Gemini-1.5, and Claude-3.5 exemplify the impressive capabilities of ...

Building on MM1’s success, Apple’s new paper, MM1.5: Methods, Analysis & Insights from Multimodal LLM Fine-tuning, introduces an improved model family aimed at enhancing capabilities in text-rich ...

In a new paper FACTS About Building Retrieval Augmented Generation-based Chatbots, an NVIDIA research team introduces the FACTS framework, designed to create robust, secure, and enterprise-grade ...

In a new paper FACTS About Building Retrieval Augmented Generation-based Chatbots, an NVIDIA research team introduces the FACTS framework, designed to create robust, secure, and enterprise-grade ...

Sparse Mixture of Experts (MoE) models are gaining traction due to their ability to enhance accuracy without proportionally increasing computational demands. Traditionally, significant computational ...

Tools designed for rewriting, refactoring, and optimizing code should prioritize both speed and accuracy. Large language models (LLMs), however, often lack these critical attributes. Despite these ...