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The paper proposes ModelLog, a declarative probabilistic framework for evaluating language models by defining semantic constraints over token predictions, linking evaluation to learning through shared semantics.
The paper proposes a neurosymbolic architecture for learning to assemble novel structures by incorporating semantic constraints from natural language and visual demonstrations, achieving more data-efficient online adaptation.