Reasoning over TKGs has two challenges: 1.

Large language models (LLMs) can be seen as atomic units of computation mapping sequences to a distribution over sequences.

Recently, Chiang et al. (2023) showed that finite-precision transformer classifiers

We give a model-based agent that builds a Python program representing its knowledge of the world based on its interactions with the environment.

In this work, we aim to leverage prior symbolic knowledge to improve the performance ofdeepmodels.

Many (if not all) real-world applications require efficient handling of uncertainty and a compact representation of a wide variety of knowledge. Indeed, complex concepts and relationships that typically comprise expert knowledge may be difficult to express in graphical models but can be represented compactly using classical logic.

Regularization terms, suchasL2 regularization leading toweightdecay,arecommonly employed during training to improve a model's generalization capabilities.