LMEnt: A Suite for Analyzing Knowledge in Language Models from
  Pretraining Data to Representations
Authors: name: Daniela Gottesman, name: Alon Gilae-Dotan, name: Ido Cohen, name: Yoav Gur-Arieh, name: Marius Mosbach, name: Ori Yoran, name: Mor Geva

Language models (LMs) increasingly drive real-world applications that require
world knowledge. However, the internal processes through which models turn data
into representations of knowledge and beliefs about the world, are poorly
understood. Insights into these processes could pave the way for developing LMs
with knowledge representations that are more consistent, robust, and complete.
To facilitate studying these questions, we present LMEnt, a suite for analyzing
knowledge acquisition in LMs during pretraining. LMEnt introduces: (1) a
knowledge-rich pretraining corpus, fully annotated with entity mentions, based
on Wikipedia, (2) an entity-based retrieval method over pretraining data that
outperforms previous approaches by as much as 80.4%, and (3) 12 pretrained
models with up to 1B parameters and 4K intermediate checkpoints, with
comparable performance to popular open-sourced models on knowledge benchmarks.
Together, these resources provide a controlled environment for analyzing
connections between entity mentions in pretraining and downstream performance,
and the effects of causal interventions in pretraining data. We show the
utility of LMEnt by studying knowledge acquisition across checkpoints, finding
that fact frequency is key, but does not fully explain learning trends. We
release LMEnt to support studies of knowledge in LMs, including knowledge
representations, plasticity, editing, attribution, and learning dynamics.