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In this case we build a model with 1T index which we lookup for every token to make prediction with much smaller model.
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This index seems to be used to minimize the size of models.
I'm familiar with term indexing as described in The Handbook of Automated Reasoning and I imagine that this index helps them recognize 'generalizations'.
In the way that a rewrite rule can be used to reduce an infinite number of expressions, not just a single expression, a generalization can be used to minimize models.
Generally, such an index would be some kind of prefix-tree.
This index seems to be used to minimize the size of models.
I'm familiar with term indexing as described in The Handbook of Automated Reasoning and I imagine that this index helps them recognize 'generalizations'.
In the way that a rewrite rule can be used to reduce an infinite number of expressions, not just a single expression, a generalization can be used to minimize models.
Generally, such an index would be some kind of prefix-tree.
Just a guess, guessing is fun