Large language models evolved alongside deep-learning neural networks and are critical to generative AI. Here's a first look, including the top LLMs and what they're used for today.

More information: Valentin Hofmann et al, Derivational morphology reveals analogical generalization in large language models, Proceedings of the National Academy of Sciences (2025). DOI: 10.1073 ...

"For example, robots should accurately assess whether text is ... we strongly believe that harnessing large-language models for scene understanding, alongside synthetic scene representations, holds ...

Many NLP applications are built on language representation models (LRM) designed to understand and generate human language. Examples of such models include GPT (Generative Pre-trained Transformer ...

Discover the key differences between human cognition and AI processing, and how their unique strengths can complement each ...

While it’s still very much an emerging field, early providers include QueryPal, Promptable, Rebuff and TrueLens. As prompt ops evolve, these platforms will continue to iterate, improve and provide ...

Retrieval-augmented generation, or RAG, integrates external data sources to reduce hallucinations and improve the response accuracy of large language models. Retrieval-augmented generation (RAG ...

On Monday, a group of university researchers released a new paper suggesting that fine-tuning an AI language model (like the one that powers ChatGPT) on examples of insecure code can lead to ...

To train its Gemini 1.0 Ultra model, for example, Google reportedly spent $191 million. Large language models (LLMs) also require considerable computational power each time they answer a request ...