Large Language Models

Some voters navigated last week’s United States elections with help from a large language model that generated output based on verified, nonpartisan information.

A new open source large language model outperforms competitors, including the open-weights Llama 3.1 405B, on a variety of benchmarks.

Coding agents are improving, but can they tackle machine learning tasks? 

A new report documents the interplay of powerful forces that drove AI over the past year: open versus proprietary technology, public versus private financing, innovation versus caution. 

The parade of ever more capable LLMs continues with Qwen 2.5.

Studies have established that large language models can memorize the text passages they’ve been trained on repeatedly and regurgitate them when prompted in adversarial and, though rarely, in benign ways.

Amazon took on talent and technology from robotics startup Covariant to enhance its warehouse automation, an area critical to its core ecommerce business.

SambaNova raised the speed limit for access to the largest model in the Llama 3.1 family — and it’s free.

Preliminary versions of OpenAI’s new model family were trained explicitly to think step-by-step, yielding outstanding marks in math, science, and coding — but users can’t see their reasoning steps.

Researchers have probed the inner workings of individual layers of large language models. A new tool applies this approach to all layers.

How often do large language models make up information when they generate text based on a retrieved document? A study evaluated the tendency of popular models to hallucinate while performing retrieval-augmented generation (RAG). 

A new model generates tokens faster than current transformers, especially when processing long inputs.

An agentic coding assistant boosted the state of the art in an important benchmark by more than 30 percent.