Large language models have moved out of the research lab and into engineers’ daily workflow. LLMs serve as reasoning engines ...

You know what’s cheaper than large language models? Small language models, which are designed for specialized tasks and can ...

Small Language Models or SLMs are on their way toward being on your smartphones and other local devices, be aware of what's coming. In today’s column, I take a close look at the rising availability ...

A systematic comparison of large language models suggests that larger models align better with both human behavior and brain activity during natural reading. Instruction tuning, however, does not ...

The advent of large language models (LLMs) has started to reshape many technology development efforts and research roadmaps. Apart from transforming the space of natural language processing, LLMs have ...

The proliferation of edge AI will require fundamental changes in language models and chip architectures to make inferencing and learning outside of AI data centers a viable option. The initial goal ...

Gary Marcus, professor emeritus at NYU, explains the differences between large language models and "world models" — and why he thinks the latter are key to achieving artificial general intelligence.

Bigger has defined AI from day one. New data says task-specific small models beat frontier LLMs on accuracy, cost and speed — and save money.

When a standard large language model (LLM) is confronted with a problem, it tries to solve it by matching it to similar information it has seen before, and then give an answer based on those past ...

AI thrives on data but feeding it the right data is harder than it seems. As enterprises scale their AI initiatives, they face the challenge of managing diverse data pipelines, ensuring proximity to ...

'The industry has become unwelcoming to inexperienced newcomers, prompting many to switch careers': Beijing-based legal officer A major legal database affiliated with Peking University has launched a ...

Companies running large language models face a persistent bottleneck: the memory consumed by key-value caches during inference grows with every token generated, forcing operators to choose between ...