This tells us that “nearly all the work” of figuring out what “dogs” are must come, not from labeled examples, but from unsupervised learning: humans looking at the world and noticing statistical patterns which other humans also notice.
Hello there! There is some overlap in your idea of natural latents and a concept I’m currently testing, which is an unsupervised RL that uses layered morphology—framing the dog problem as:
Simply, Reinforcement Learning using Layered Morphology (RLLM) is a training process that guides an language model using complex patterns outlined in a dataset. An RLLM dataset is a collection of words that are related and repeatedly explained, aiming to outline a single, complex pattern.
To illustrate, five sentences are shown below:
The dog is energetic, furry, loyal, playful, and friendly.
A dog can be affectionate, obedient, curious, protective, and agile.
This dog seems intelligent, gentle, devoted, alert, and sociable.
The dog is affectionate, loyal, playful, intelligent, and energetic.
This dog is friendly, obedient, furry, alert, and curious.
Some noticeable patterns from the five sentences and will become part of an RLLM dataset:
Using sentences repeatedly is a pattern.
Repeatedly mentioning “dog” is a pattern.
The word sequencing (eg. the word “dog” being the second word in four of the sentences.) is a pattern.
“Descriptions of a dog” is a pattern.
Always describing the dog five different ways is a pattern.
Using the same words multiple times is a pattern. (eg. loyal, affectionate, energetic, friendly, obedient and curious.)
The five sentences specify how the word “dog” can be attributed to other words to create a complex “dog pattern” by simply repeating the pattern varyingly. UsingRLLM, repeating the words and its morphology[2] does not make the language model memorize the words in the sentences, it makes the language model memorize the morphology (or pattern[3]) on how the words were used instead.[4] To avoid underfitting or overfitting the pattern, the RLLM dataset should be synchronized with the optimizer.
Hello there! There is some overlap in your idea of natural latents and a concept I’m currently testing, which is an unsupervised RL that uses layered morphology—framing the dog problem as: