  In the age of artificial intelligence, the role of large language models
(LLMs) is becoming increasingly central. Despite their growing prevalence,
their capacity to consolidate knowledge from different training documents - a
crucial ability in numerous applications - remains unexplored. This paper
presents the first study examining the capability of LLMs to effectively
combine such information within their parameter space. We introduce EpiK-Eval,
a novel question-answering benchmark tailored to evaluate LLMs' proficiency in
formulating a coherent and consistent knowledge representation from segmented
narratives. Evaluations across various LLMs reveal significant weaknesses in
this domain. We contend that these shortcomings stem from the intrinsic nature
of prevailing training objectives. Consequently, we advocate for refining the
approach towards knowledge consolidation, as it harbors the potential to
dramatically improve their overall effectiveness and performance. The findings
from this study offer insights for developing more robust and reliable LLMs.
Our code and benchmark are available at
https://github.com/chandar-lab/EpiK-Eval

Computation and Language;Artificial Intelligence