In today’s fast-paced and interconnected world, mental health is more important than ever. The constant pressures of work, social media, and global events can take a toll on our emotional and ...

The challenge lies in generating effective agentic workflows for Large Language Models (LLMs). Despite their remarkable capabilities across diverse tasks, creating workflows that combine multiple LLMs ...

The problem with efficiently linearizing large language models (LLMs) is multifaceted. The quadratic attention mechanism in traditional Transformer-based LLMs, while powerful, is computationally ...

Zyphra has officially released Zamba2-7B, a state-of-the-art small language model that promises unprecedented performance in the 7B parameter range. This model outperforms existing competitors, ...

A Model Inversion (MI) attack is a type of privacy attack on machine learning and deep learning models, where an attacker tries to invert the model’s outputs to recreate privacy-sensitive training ...

Language models (LMs) are widely utilized across domains like mathematics, coding, and reasoning to handle complex tasks. These models rely on deep learning techniques to generate high-quality outputs ...

XAI, or Explainable AI, brings about a paradigm shift in neural networks that emphasizes the need to explain the decision-making processes of neural networks, which are well-known black boxes. In XAI, ...

Predibase announces the Predibase Inference Engine, their new infrastructure offering designed to be the best platform for serving fine-tuned small language models (SLMs). The Predibase Inference ...

A major challenge in the evaluation of vision-language models (VLMs) lies in understanding their diverse capabilities across a wide range of real-world tasks. Existing benchmarks often fall short, ...

Large language models (LLMs) often fail to consistently and accurately perform multi-step reasoning, especially in complex tasks like mathematical problem-solving and code generation. Despite recent ...

Mixture of Experts (MoE) models are becoming critical in advancing AI, particularly in natural language processing. MoE architectures differ from traditional dense models by selectively activating ...

Large language models (LLMs) have greatly advanced various natural language processing (NLP) tasks, but they often suffer from factual inaccuracies, particularly in complex reasoning scenarios ...