  We reveal new methods and the theoretical foundations of techniques for
editing large language models. We also show how the new theory can be used to
assess the editability of models and to expose their susceptibility to
previously unknown malicious attacks. Our theoretical approach shows that a
single metric (a specific measure of the intrinsic dimensionality of the
model's features) is fundamental to predicting the success of popular editing
approaches, and reveals new bridges between disparate families of editing
methods. We collectively refer to these approaches as stealth editing methods,
because they aim to directly and inexpensively update a model's weights to
correct the model's responses to known hallucinating prompts without otherwise
affecting the model's behaviour, without requiring retraining. By carefully
applying the insight gleaned from our theoretical investigation, we are able to
introduce a new network block -- named a jet-pack block -- which is optimised
for highly selective model editing, uses only standard network operations, and
can be inserted into existing networks. The intrinsic dimensionality metric
also determines the vulnerability of a language model to a stealth attack: a
small change to a model's weights which changes its response to a single
attacker-chosen prompt. Stealth attacks do not require access to or knowledge
of the model's training data, therefore representing a potent yet previously
unrecognised threat to redistributed foundation models. They are
computationally simple enough to be implemented in malware in many cases.
Extensive experimental results illustrate and support the method and its
theoretical underpinnings. Demos and source code for editing language models
are available at https://github.com/qinghua-zhou/stealth-edits.
