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 has successfully learned relational knowledge during pre-training. Probing is an inexpensive way to compare LMs of different sizes and training configurations. However%2C 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%2C comparing different types of LMs becomes impossible. To address this%2C 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%2C731 instances (40%2C916 in a larger variant) from which we produce alternative statements for each relational fact%2C 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%2C BEAR%2C 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.?quality=80&w=800)
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