How LLMs stand out
LLMs, such as GPT-3 and GPT-4, are simply LMs that are trained on a very large amount of text and have a very large number of parameters. The larger the model (in terms of parameters and training data), the more capable it is of understanding and generating complex and varied texts. Here are some key ways in which LLMs differ from smaller LMs:
- Data: LLMs are trained on vast amounts of data. This allows them to learn from a wide range of linguistic patterns, styles, and topics.
- Parameters: LLMs have a huge number of parameters. Parameters in an ML model are the parts of the model that are learned from the training data. The more parameters a model has, the more complex patterns it can learn.
- Performance: Because they’re trained on more data and have more parameters, LLMs generally perform better than smaller ones. They’re capable of generating more coherent and diverse texts, and they’re better at understanding context, making...
