Direct preference alignment losses can be expressed as logical programs over model predictions, yielding an organized landscape of billions of definable losses and a route to new variants.
Declarative Design of Neural Predicates in Neuro-Symbolic Systems
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abstract
Neuro-symbolic systems (NeSy), which claim to combine the best of both learning and reasoning capabilities of artificial intelligence, are missing a core property of reasoning systems: Declarativeness. The lack of declarativeness is caused by the functional nature of neural predicates inherited from neural networks. We propose and implement a general framework for fully declarative neural predicates, which hence extends to fully declarative NeSy frameworks. We first show that the declarative extension preserves the learning and reasoning capabilities while being able to answer arbitrary queries while only being trained on a single query type.
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Understanding the Logic of Direct Preference Alignment through Logic
Direct preference alignment losses can be expressed as logical programs over model predictions, yielding an organized landscape of billions of definable losses and a route to new variants.