The attention mechanism walkthrough with concrete scores is what makes this click for people. One thing I'd add: understanding that the temperature parameter directly controls that sampling roulette wheel is huge for anyone building on top of LLMs. Lower temperature = tighter distribution = more predictable outputs. I run multiple AI coding agents in production daily, and tuning temperature is the difference between getting reliable code generation vs. creative hallucinations. The autoregressive generation loop also explains why context window management matters so much — every extra token compounds the computation cost.
While this is a solid explanation of transformer architecture and function, your statement regarding how humans "naturally" form throughts and convert them into language is incredibly oversimplified. If you want to compare the similarities and differences between human cognition and LLM function, you actually need to understand how humans cognate and form linguistic representations.
Key Processes in Thought and Speech Generation:
Thought Formation: Complex thoughts emerge from interconnected, dynamic neural patterns that act as a "lightning storm" of activity. These, for the most part, are processed in high-level cognitive, non-verbal areas.
Word Selection & Sequencing: The posterior-superior temporal gyrus (P-STG) serves as a core language hub to select words and arrange them into linguistic structures, transitioning from abstract ideas to linguistic components.
Neural Encoding Cascade: Research indicates a specific, fast-acting sequence where neurons activate:
400 ms before speech: Morphemes (meaning units) activate.
200 ms before speech: Phonemes (consonant/vowel sounds) activate.
70 ms before speech: Syllables (units of pronunciation) activate.
Broca's Area and Motor Cortex: These areas are responsible for planning the speech response and controlling the muscles in the throat, mouth, and lips to produce sound.
Brain-Computer Interfaces (BCI): Researchers have developed technologies that can decode these electrical signals directly from the motor cortex to translate, in near real-time, intended movements of the vocal tract into text or audible speech.
This process involves a hierarchical structure where abstract thoughts are translated into a sequence of linguistic, then acoustic, representations.
Now, we can begin to have a conversation that matters about the differences and similarities between human language generation and LLMs.
The attention mechanism walkthrough with concrete scores is what makes this click for people. One thing I'd add: understanding that the temperature parameter directly controls that sampling roulette wheel is huge for anyone building on top of LLMs. Lower temperature = tighter distribution = more predictable outputs. I run multiple AI coding agents in production daily, and tuning temperature is the difference between getting reliable code generation vs. creative hallucinations. The autoregressive generation loop also explains why context window management matters so much — every extra token compounds the computation cost.
Hi, good job on documenting the LLM process, but I encountered a technical issue in a sentence.
"The overall vocabulary typically contains 50,000 to 100,000 unique tokens that the model learned during training."
Well, as far as I know, the model doesn't learn the vocab during the training phase. It's a preprocessing phase.
So the vocabulary is created before model training, not during.
While this is a solid explanation of transformer architecture and function, your statement regarding how humans "naturally" form throughts and convert them into language is incredibly oversimplified. If you want to compare the similarities and differences between human cognition and LLM function, you actually need to understand how humans cognate and form linguistic representations.
Key Processes in Thought and Speech Generation:
Thought Formation: Complex thoughts emerge from interconnected, dynamic neural patterns that act as a "lightning storm" of activity. These, for the most part, are processed in high-level cognitive, non-verbal areas.
Word Selection & Sequencing: The posterior-superior temporal gyrus (P-STG) serves as a core language hub to select words and arrange them into linguistic structures, transitioning from abstract ideas to linguistic components.
Neural Encoding Cascade: Research indicates a specific, fast-acting sequence where neurons activate:
400 ms before speech: Morphemes (meaning units) activate.
200 ms before speech: Phonemes (consonant/vowel sounds) activate.
70 ms before speech: Syllables (units of pronunciation) activate.
Broca's Area and Motor Cortex: These areas are responsible for planning the speech response and controlling the muscles in the throat, mouth, and lips to produce sound.
Brain-Computer Interfaces (BCI): Researchers have developed technologies that can decode these electrical signals directly from the motor cortex to translate, in near real-time, intended movements of the vocal tract into text or audible speech.
This process involves a hierarchical structure where abstract thoughts are translated into a sequence of linguistic, then acoustic, representations.
Now, we can begin to have a conversation that matters about the differences and similarities between human language generation and LLMs.