Talk:LLM Prompt/Response Code-Event Chain

LLM Prompt/Response Code-Event Chain

I. User Input Preprocessing

A. Tokenization and Normalization

• The user's query or instruction is 'inputted' into the system via a graphical user interface (GUI), command-line interface (CLI), or voice user interface (VUI).
• The input is then tokenized and normalized, breaking it down into individual tokens, which are typically in the form of subwords.
• Text preprocessing techniques, such as stemming or lemmatization, may be applied to reduce words to their base form.

II. Model Initialization

A. Embedding Layer Initialization

• The LLM model is initialized with its pre-trained weights and biases, which are typically stored in a weight matrix.
• The embedding layer, responsible for mapping input tokens to dense vector representations, is initialized with pre-trained embeddings or randomly initialized parameters.

III. Contextualization

A. Attention Mechanisms

• The LLM model uses various attention mechanisms, such as self-attention or multi-head attention, to weigh the importance of different input tokens.
• The output of the contextualization process is a dense vector representation of the input, which captures its semantic meaning.

IV. Response Generation

A. Sampling and Decoding

• The LLM model generates a response by sampling from the output distribution of the final layer, using techniques such as greedy search or beam search.
• The sampled output is then decoded into a sequence of tokens, which form the generated response.

V. Post-processing

A. Output Formatting

• The generated response may undergo various post-processing steps, such as tokenization or stopword removal, to produce a final output.
• The output is then returned to the user, who can interact with it using the system's interface.