What does "Hidden States" mean?
Table of Contents
Hidden states are elements within neural networks that hold important information during processing. They are not visible directly, but they help the model understand and make sense of inputs.
Role of Hidden States
In many models, especially language models, hidden states capture the context and relationships between different pieces of information. They allow the model to remember what it has seen so far and to use that memory when generating responses or making predictions.
Importance in Language Models
For language models, hidden states play a crucial role in how well the model performs tasks like answering questions or generating text. When a model processes a sentence, it creates hidden states that reflect its understanding of the words and their meanings.
How They Work
As a model reads or generates text, it updates these hidden states. Each hidden state can contain different types of information, such as relationships between words or the overall meaning of a sentence. This helps the model make more accurate decisions as it processes further text.
Applications
Understanding hidden states can improve how we use language models in various applications. By examining these states, researchers can figure out how well a model understands language and can even find ways to reduce mistakes, like generating false information.
Summary
Hidden states are a fundamental part of how neural networks, particularly language models, work. They store important information that helps models understand and generate text effectively. By studying these hidden states, we can enhance model performance and reliability.