From input to output – understanding LLM response generation
The process of generating a response in an LLM such as GPT-4 is a complex journey from input to output. In this section, we’ll take a closer look at the steps that are involved.
Input processing
The following are the key preprocessing steps in LLMs:
- Tokenization: Splitting the text into tokens based on predefined rules or learned patterns.
- Embedding: Sometimes, tokens are normalized to a standard form. For instance, “USA” and “U.S.A.” might be normalized to a single form.
- Positional encoding: Each unique token is associated with an index in a vocabulary list. The model will use these indices, not the text itself, to process the language.
Model architecture
The following are central components in the architecture of LLMs:
- Transformer blocks: Each Transformer block contains two main parts: a multi-head self-attention mechanism and a position-wise...
