this post was submitted on 29 Nov 2023
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Machine Learning
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The point of the paper is that LLMs memorize an insane amount of training data and, with some massaging, can be made to output it verbatim. If that training data has PII (personally identifiable information), you're in trouble.
Another big takeaway is that training for more epochs leads to more memorization.
How is that a problem? The entire point of training is to memorize and generalize the training data.
Learning English is not simply memorizing a billion sample sentences.
The problem is that we want it to learn to string words together for itself, not regurgitate words which already appear in the training set in that order.
This paper attempts to solve the difficult dilemma of detecting how much of the success of an llm is due to rote memorization.
Maybe more importantly: how much parameter space/ training resources are wasted on this?