Large Language Model (LLM)
A Large Language Model is a neural network trained on vast amounts of text data to predict and generate human language. LLMs are characterized by their scale—typically containing billions of parameters—which enables them to capture complex patterns in language and perform a wide range of natural language tasks including text generation, translation, question answering, and reasoning across diverse domains.
Retrieval Augmented Generation (RAG)
Retrieval Augmented Generation is a technique that enhances LLM capabilities by integrating external information retrieval with language generation to reduce hallucinations and ground outputs in factual data.
Emerging Architectures: Diffusion-Based LLMs
Recent developments challenge the dominance of autoregressive transformers, introducing iterative diffusion mechanisms for text generation:
- DiffusionGemma: Google DeepMind released DiffusionGemma, an open-weights LLM under the Apache 2.0 license that utilizes iterative diffusion rather than standard next-token prediction.
- Signifies a shift towards “thinking” via diffusion steps, potentially offering new paradigms for coherence and reasoning in text synthesis.
- Part of the broader Gemma family of models from Google DeepMind.