---
title: "Generalization"
type: concept
source: https://www.jemoka.com/posts/kbhdomains_of_generalization/
confidence: high
status: active
---

Compositionality Getting the right contents.
semantic capacity tested &mdash; knowing the propositional content operationalization &mdash; form =&gt; meaning mapping measure of success &mdash; generalizing to the right meaning representation for novel expressions (this is non-trivial; multiple compatible generalization maybe applicable depending on context) task Given that a model can map certain expressions to their meaning representations, can they also do this for new expressions?
results lexical generalization (fill in new words/pairs) isn&rsquo;t too hard for new NN
but structural generalization (permute syntactical forms) is very hard
subclause decomposition
phrase id
iterative proposional phrase and noun phrase annotation
verb phrase normalization
limitations &ldquo;structure&rdquo; requires some definition by committing to a particular formalism PTing makes exposing these stems hard Entity Tracking Getting the right entity through a conversation.
task &ldquo;box world&rdquo;: putting stuff into boxes
results models that did have a lot of code in pretraining generalized better than those that didn&rsquo;t this is even shown in minimal pairs of the same architecture with more code
