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Questions and Answers
What is a key innovation of LLMs?
What type of written material can be used to train LLMs?
How does an LLM learn to make better predictions?
What does the analogy of the shower faucet represent?
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How many weight parameters does the most powerful version of GPT-3 have?
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What happens to the weight parameters of an LLM when it is first initialized?
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In the shower faucet analogy, what do the different faucets represent?
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What is the purpose of adjusting the weight parameters during LLM training?
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What happens to the adjustments made to the weight parameters as the LLM gets closer to the correct prediction?
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What is the key difference between LLM training and traditional supervised learning?
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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.
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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.