  Knowledge-intensive language understanding tasks require Language Models
(LMs) to integrate relevant context, mitigating their inherent weaknesses, such
as incomplete or outdated knowledge. Nevertheless, studies indicate that LMs
often ignore the provided context as it can conflict with the pre-existing LM's
memory learned during pre-training. Moreover, conflicting knowledge can already
be present in the LM's parameters, termed intra-memory conflict. Existing works
have studied the two types of knowledge conflicts only in isolation. We
conjecture that the (degree of) intra-memory conflicts can in turn affect LM's
handling of context-memory conflicts. To study this, we introduce the DYNAMICQA
dataset, which includes facts with a temporal dynamic nature where a fact can
change with a varying time frequency and disputable dynamic facts, which can
change depending on the viewpoint. DYNAMICQA is the first to include real-world
knowledge conflicts and provide context to study the link between the different
types of knowledge conflicts. With the proposed dataset, we assess the use of
uncertainty for measuring the intra-memory conflict and introduce a novel
Coherent Persuasion (CP) score to evaluate the context's ability to sway LM's
semantic output. Our extensive experiments reveal that static facts, which are
unlikely to change, are more easily updated with additional context, relative
to temporal and disputable facts.
