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The word 'Lead' has three definitions. When an LLM model tokenizes this word, it is always the same token. Imagine being able to put any particular embedding at any particular time into a 'Quantum State'. When an Embedding is in a Quantum State, the word token could have up to 3 different meanings (x1, x2, x3). The Quantum State gets collapsed based on the individual context surrounding the word. 'Jill lead Joy to the store' would collapse to x1. 'Jill and Joy stumbled upon a pile of lead' would collapse to x3. Very simple, right? This method produces OFF THE CHARTS results:
The word 'Lead' has three definitions. When an LLM model tokenizes this word, it is always the same token. Imagine being able to put any particular embedding at any particular time into a 'Quantum State'. When an Embedding is in a Quantum State, the word token could have up to 3 different meanings (x1, x2, x3). The Quantum State gets collapsed based on the individual context surrounding the word. 'Jill lead Joy to the store' would collapse to x1. 'Jill and Joy stumbled upon a pile of lead' would collapse to x3. Very simple, right? This method produces OFF THE CHARTS results:
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https://www.youtube.com/watch?v=tuQI6A-EOqE
https://www.youtube.com/watch?v=tuQI6A-EOqE
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The word 'Lead' has three definitions. When an LLM model tokenizes this word, it is always the same token. Imagine being able to put any particular embedding at any particular time into a 'Quantum State'. When an Embedding is in a Quantum State, the word token could have up to 3 different meanings (x1, x2, x3). The Quantum State gets collapsed based on the individual context surrounding the word. 'Jill lead Joy to the store' would collapse to x1. 'Jill and Joy stumbled upon a pile of lead' would collapse to x3. Very simple, right? This method produces OFF THE CHARTS results:


https://www.youtube.com/watch?v=tuQI6A-EOqE 
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maximuspowers maier-s nicolollo
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John6666