DisentQA: Disentangling Parametric and Contextual Knowledge with Counterfactual Question Answering

Understand

Question answering models commonly have access to two sources of "knowledge" during inference time: (1) parametric knowledge - the factual knowledge encoded in the model weights, and (2) contextual knowledge - external knowledge (e.g., a Wikipedia passage) given to the model to generate a grounded answer.

• Having these two sources of knowledge entangled together is a core issue for generative QA models as it is unclear whether the answer stems from the given non-parametric knowledge or not.
• This unclarity has implications on issues of trust, interpretability and factuality.
• In this work, we propose a new paradigm in which QA models are trained to disentangle the two sources of knowledge.