A co-evolutionary model of lexical meanings and composition under simplicity and accuracy pressures shows conservativity in quantifiers emerges as an efficient abstraction on the Pareto frontier.
and Tenenbaum, Joshua B
3 Pith papers cite this work. Polarity classification is still indexing.
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LLMs perform in-context learning as trajectories through a structured low-dimensional conceptual belief space, with the structure visible in both behavior and internal representations and causally manipulable via interventions.
Language models require explicit examples to learn zero in arithmetic but language pretraining halves the examples needed.
citing papers explorer
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Compositionality and the lexicon in evolutionary semantics
A co-evolutionary model of lexical meanings and composition under simplicity and accuracy pressures shows conservativity in quantifiers emerges as an efficient abstraction on the Pareto frontier.
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LLMs perform in-context learning as trajectories through a structured low-dimensional conceptual belief space, with the structure visible in both behavior and internal representations and causally manipulable via interventions.
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Nothing from Something: Can a Language Model Discover 0?
Language models require explicit examples to learn zero in arithmetic but language pretraining halves the examples needed.