  The model editing problem concerns how language models should learn new facts
about the world over time. While empirical research on model editing has drawn
widespread attention, the conceptual foundations of model editing remain shaky
-- perhaps unsurprisingly, since model editing is essentially belief revision,
a storied problem in philosophy that has eluded succinct solutions for decades.
Model editing nonetheless demands a solution, since we need to be able to
control the knowledge within language models. With this goal in mind, this
paper critiques the standard formulation of the model editing problem and
proposes a formal testbed for model editing research. We first describe 12 open
problems with model editing, based on challenges with (1) defining the problem,
(2) developing benchmarks, and (3) assuming LLMs have editable beliefs in the
first place. Many of these challenges are extremely difficult to address, e.g.
determining far-reaching consequences of edits, labeling probabilistic
entailments between facts, and updating beliefs of agent simulators. Next, we
introduce a semi-synthetic dataset for model editing based on Wikidata, where
we can evaluate edits against labels given by an idealized Bayesian agent. This
enables us to say exactly how belief revision in language models falls short of
a desirable epistemic standard. We encourage further research exploring
settings where such a gold standard can be compared against. Our code is
publicly available at: https://github.com/peterbhase/LLM-belief-revision
