  Large language models (LLMs) store extensive factual knowledge, but the
mechanisms behind how they store and express this knowledge remain unclear. The
Knowledge Neuron (KN) thesis is a prominent theory for explaining these
mechanisms. This theory is based on the knowledge localization (KL) assumption,
which suggests that a fact can be localized to a few knowledge storage units,
namely knowledge neurons. However, this assumption may be overly strong
regarding knowledge storage and neglects knowledge expression mechanisms. Thus,
we re-examine the KL assumption and confirm the existence of facts that do not
adhere to it from both statistical and knowledge modification perspectives.
Furthermore, we propose the Query Localization (QL) assumption. (1) Query-KN
Mapping: The localization results are associated with the query rather than the
fact. (2) Dynamic KN Selection: The attention module contributes to the
selection of KNs for answering a query. Based on this, we further propose the
Consistency-Aware KN modification method, which improves the performance of
knowledge modification. We conduct 39 sets of experiments, along with
additional visualization experiments, to rigorously validate our conclusions.
