Understanding Large Language Models (LLMs)

Study Notes

Understanding Large Language Models (LLMs)

LLMs utilize hidden state vectors, which change as data is processed through layers, facilitating information tracking.
Modern LLMs have large dimensional vectors; GPT-3, for instance, uses vectors with 12,288 dimensions per word.
This dimensionality is significantly larger than previous models, such as Google's word2vec, which had 600 dimensions.
Extra dimensions act as "scratch space," allowing LLMs like GPT-3 to store contextual notes for each word.

Layer Interaction and Contextual Information

Information encoded in the vectors can evolve as it moves between layers, refining the model’s understanding.
For example, the 60th layer might produce a vector for a character, John, detailing attributes like marital status and location as a list of numbers.
Other words in the story, like Cheryl or wallet, may also carry contextual information through their respective vectors.

Model Architecture and Learning Focus

LLMs typically consist of many layers; GPT-3 has 96 layers, allowing for complex processing.
Initial layers emphasize syntactic comprehension and ambiguity resolution.
Subsequent layers focus on discerning higher-level meanings, integrating character details such as gender, relationships, locations, and objectives throughout a narrative.

Practical Considerations

The analysis and descriptions of LLMs in diagrams are often hypothetical and meant to illustrate concepts rather than depict exact model behavior.
Real language models are extensively researched and show far richer capabilities than simple representations allow.

Description

Learn about how Large Language Models (LLMs) keep track of information by modifying hidden state vectors as they pass through layers. Explore the use of extremely large word vectors, like the 12,288 dimensions used in GPT-3.