Exploring Human-Like Reasoning Through Model Synthesis Architecture
By
PaulHoule
Crackling crust, pillowy middle. The kind of bagel that earns a second cup of coffee.
Summary
The article explores how people synthesize probabilistic models to handle novel situations by combining distributed and symbolic representations. It introduces a 'Model Synthesis Architecture' (MSA) that uses language models for relevance-based retrieval and probabilistic programs for coherent reasoning. The MSA is evaluated against human judgments in a 'Model Olympics' dataset, demonstrating its effectiveness in open-ended reasoning.
Key quotes
· 4 pulledWhen faced with novel situations, people are able to marshal relevant considerations from a wide range of background knowledge and put these to use in inferences and predictions.
We propose a computational implementation of this idea -- a 'Model Synthesis Architecture' (MSA) -- using language models to implement global relevance-based retrieval and model synthesis and probabilistic programs to implement bespoke, coherent world models.
Our MSA approach captures human judgments better than language model-only baselines, under both direct and chain-of-thought generations from the LM that supports model synthesis.
These results suggest that MSAs can be implemented in a way that mirrors people's ability to deliver locally coherent reasoning over globally relevant variables.
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