  Since the development of Large Language Models (LLMs) has achieved remarkable
success, understanding and controlling their internal complex mechanisms has
become an urgent problem. Recent research has attempted to interpret their
behaviors through the lens of inner representation. However, developing
practical and efficient methods for applying these representations for general
and flexible model editing remains challenging. In this work, we explore how to
use representation engineering methods to guide the editing of LLMs by
deploying a representation sensor as an oracle. We first identify the
importance of a robust and reliable sensor during editing, then propose an
Adversarial Representation Engineering (ARE) framework to provide a unified and
interpretable approach for conceptual model editing without compromising
baseline performance. Experiments on multiple model editing paradigms
demonstrate the effectiveness of ARE in various settings. Code and data are
available at
https://github.com/Zhang-Yihao/Adversarial-Representation-Engineering.
