Measuring the Knowledge Acquisition-Utilization Gap in Pretrained
  Language Models
Amirhossein Kazemnejad Mehdi Rezagholizadeh Prasanna Parthasarathi Sarath Chandar
While pre-trained language models (PLMs) have shown evidence of acquiring
vast amounts of knowledge, it remains unclear how much of this parametric
knowledge is actually usable in performing downstream tasks. We propose a
systematic framework to measure parametric knowledge utilization in PLMs. Our
framework first extracts knowledge from a PLM's parameters and subsequently
constructs a downstream task around this extracted knowledge. Performance on
this task thus depends exclusively on utilizing the model's possessed
knowledge, avoiding confounding factors like insufficient signal. As an
instantiation, we study factual knowledge of PLMs and measure utilization
across 125M to 13B parameter PLMs. We observe that: (1) PLMs exhibit two gaps -
in acquired vs. utilized knowledge, (2) they show limited robustness in
utilizing knowledge under distribution shifts, and (3) larger models close the
acquired knowledge gap but the utilized knowledge gap remains. Overall, our
study provides insights into PLMs' capabilities beyond their acquired
knowledge.