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.

New reasoning models have something interesting and compelling called “chain of thought.” What that means, in a nutshell, is that the engine spits out a line of text attempting to tell the user what ...

There’s a trade-off between the accuracy and sustainability of different large language models, according to German ...

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 ...

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 ...

MIT researchers developed SEAL, a framework that lets language models continuously learn new knowledge and tasks.

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

For example, AI21 Labs in April debuted a model called Jamba, an intriguing combination of transformers with a second neural network called a state space model (SSM). The mixture has allowed Jamba ...

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 ...