The transformative impact of Transformers on natural language processing (NLP) and computer vision (CV) is undeniable. Their scalability and effectiveness have propelled advancements across these ...

The Transformer architecture, introduced by Vaswani et al. in 2017, serves as the backbone of contemporary language models. Over the years, numerous modifications to this architecture have been ...

Recent advancements in training large multimodal models have been driven by efforts to eliminate modeling constraints and unify architectures across domains. Despite these strides, many existing ...

Large Language Models (LLMs) have become indispensable tools for diverse natural language processing (NLP) tasks. Traditional LLMs operate at the token level, generating output one word or subword at ...

In a new paper Gemma Scope: Open Sparse Autoencoders Everywhere All At Once on Gemma 2, a Google DeepMind research team introduces Gemma Scope, a comprehensive suite of JumpReLU SAEs.

Recent advancements in large language models (LLMs) have primarily focused on enhancing their capacity to predict text in a forward, time-linear manner. However, emerging research suggests that ...

Language models (LMs) based on transformers have become the gold standard in natural language processing, thanks to their exceptional performance, parallel processing capabilities, and ability to ...

Navigation is a fundamental skill for any visually-capable organism, serving as a critical tool for survival. It enables agents to locate resources, find shelter, and avoid threats. In humans, ...

While large language models (LLMs) dominate the AI landscape, Small-scale Large Language Models (SLMs) are gaining traction as cost-effective and efficient alternatives for various applications.