On Entity Identification in Language Models
Journal:
arXiv
Published Date:
Jun 3, 2025
Abstract
We analyze the extent to which internal representations of language models
(LMs) identify and distinguish mentions of named entities, focusing on the
many-to-many correspondence between entities and their mentions. We first
formulate two problems of entity mentions -- ambiguity and variability -- and
propose a framework analogous to clustering quality metrics. Specifically, we
quantify through cluster analysis of LM internal representations the extent to
which mentions of the same entity cluster together and mentions of different
entities remain separated. Our experiments examine five Transformer-based
autoregressive models, showing that they effectively identify and distinguish
entities with metrics analogous to precision and recall ranging from 0.66 to
0.9. Further analysis reveals that entity-related information is compactly
represented in a low-dimensional linear subspace at early LM layers.
Additionally, we clarify how the characteristics of entity representations
influence word prediction performance. These findings are interpreted through
the lens of isomorphism between LM representations and entity-centric knowledge
structures in the real world, providing insights into how LMs internally
organize and use entity information.
