Facts as Experts: Adaptable and Interpretable Neural Memory over Symbolic Knowledge

Understand

Massive language models are the core of modern NLP modeling and have been shown to encode impressive amounts of commonsense and factual information.

• However, that knowledge exists only within the latent parameters of the model, inaccessible to inspection and interpretation, and even worse, factual information memorized from the training corpora is likely to become stale as the world changes.
• Knowledge stored as parameters will also inevitably exhibit all of the biases inherent in the source materials.
• To address these problems, we develop a neural language model that includes an explicit interface between symbolically interpretable factual information and subsymbolic neural knowledge.