  Knowledge probing assesses to which degree a language model (LM) has
successfully learned relational knowledge during pre-training. Probing is an
inexpensive way to compare LMs of different sizes and training configurations.
However, previous approaches rely on the objective function used in
pre-training LMs and are thus applicable only to masked or causal LMs. As a
result, comparing different types of LMs becomes impossible. To address this,
we propose an approach that uses an LM's inherent ability to estimate the
log-likelihood of any given textual statement. We carefully design an
evaluation dataset of 7,731 instances (40,916 in a larger variant) from which
we produce alternative statements for each relational fact, one of which is
correct. We then evaluate whether an LM correctly assigns the highest
log-likelihood to the correct statement. Our experimental evaluation of 22
common LMs shows that our proposed framework, BEAR, can effectively probe for
knowledge across different LM types. We release the BEAR datasets and an
open-source framework that implements the probing approach to the research
community to facilitate the evaluation and development of LMs.
