Transforming Vectors into LLM Responses
Last updated
Last updated
Now that you're familiar with word vectors and the significance of context, it's vital to grasp other fundamental components like tokenizers and detokenizers before we investigate the sophisticated processes that power Large Language Models (LLMs).
Consider a tokenizer as a "sentence divider." Its job is to dissect sentences into smaller fragments such as words, characters, subwords, or symbols that the model can process. The specific approach varies based on the model's design and size. Conversely, detokenizers perform the opposite function; they assemble the pieces outputted by the LLM into coherent sentences that we can comprehend. This step is essential for LLMs to convert human language into executable actions.
Upcoming: Informative Video by Mike Chambers from AWS To further enhance your understanding, we will turn to an instructive video by Mike Chambers. In this presentation, he clarifies the chain of events triggered by your 'prompt' (the text input you provide) to an LLM.
While the underlying mathematics might be complex, the main objective remains simple: predicting words. The video will walk you through the process of how your prompts are handled to produce intelligible text responses. This serves as a precursor to our forthcoming discussions about Prompt Engineering and LLM workflows. By doing so, we aim to present a unified view of the operational aspects of these models.
(Credits: Build on AWS)
Here you see how a Large Language Model’s job is to predict the next word based on the context.
Now that you understand the role of "context," you might want to grasp some concepts to appreciate how these models work at a granular level. These are bonus resources that are not necessary for you to complete, given the timelines of this course.
Attention in Large Language Models: Imagine being in a room where multiple conversations are happening. Your ability to focus on one conversation over the others is similar to how Attention works in neural networks. It allows the model to 'focus' on relevant parts of the input for tasks.
Encoder-Decoder Architecture: In this, an encoder translates the input (e.g., a sentence) into a fixed-size context vector. The decoder takes this context vector to generate an output sequence (e.g., a translated sentence). When the attention mechanism is in action, it guides the Decoder to focus on certain parts of the Encoder’s output, enhancing the translation or text generation task. The concept of Attention complements the Encoder-Decoder architecture, making it more effective and efficient. This architecture is a building block for LLMs such as GPT-3.5.
If you're interested in delving further into the details, you may find the following links on embeddings, attention mechanisms, and encoder-decoder architecture beneficial. A foundational understanding of neural networks, backpropagation, the softmax function, and cross-entropy will enhance your comprehension of these resources. These topics are not the primary focus of this course, so they're provided as bonus links.
Understanding Transformers: Check the Bonus Module Right Ahead.
Attention mechanism: Overview | Short Video by Google Cloud
Word2Vec and Word Embeddings | Video by StatQuest
Seq2Seq Encoder-Decoder Neural Networks | Video by StatQuest
Attention is all you need | Read the Paper on ArXiv
Attention is all you need | Watch the seminar by Stanford Online