Final answer:
Large language models are a specialized type of neural network trained on a vast corpus of text to handle language tasks, whereas neural networks are a broader class of computational models that can learn from data to perform various tasks.
Step-by-step explanation:
The difference between a large language model like GPT-3 and a general neural network lies in their architecture and purpose. A neural network is a broader concept referring to a computational model designed to process information in a way that is similar to the human brain's neurons. It's a structure comprised of layers of interconnected nodes, or 'neurons', that can learn to perform a variety of tasks through training.
A large language model, on the other hand, is a specific type of neural network that's been trained on a vast amount of text data. Its primary purpose is to understand, generate, and translate human language. Large language models can be thought of as advanced implementations of neural networks specialized to handle language-related tasks, often based on a transformer architecture which allows them to manage long-range dependencies in text.
Therefore, while all large language models are neural networks, not all neural networks are large language models. The difference mainly lies in the application, scale, and specific architecture used to address the complexities involved in understanding and generating human language.