  Large Language Models (LLMs) are advancing at a remarkable pace, with myriad
applications under development. Unlike most earlier machine learning models,
they are no longer built for one specific application but are designed to excel
in a wide range of tasks. A major part of this success is due to their huge
training datasets and the unprecedented number of model parameters, which allow
them to memorize large amounts of information contained in the training data.
This memorization goes beyond mere language, and encompasses information only
present in a few documents. This is often desirable since it is necessary for
performing tasks such as question answering, and therefore an important part of
learning, but also brings a whole array of issues, from privacy and security to
copyright and beyond. LLMs can memorize short secrets in the training data, but
can also memorize concepts like facts or writing styles that can be expressed
in text in many different ways. We propose a taxonomy for memorization in LLMs
that covers verbatim text, facts, ideas and algorithms, writing styles,
distributional properties, and alignment goals. We describe the implications of
each type of memorization - both positive and negative - for model performance,
privacy, security and confidentiality, copyright, and auditing, and ways to
detect and prevent memorization. We further highlight the challenges that arise
from the predominant way of defining memorization with respect to model
behavior instead of model weights, due to LLM-specific phenomena such as
reasoning capabilities or differences between decoding algorithms. Throughout
the paper, we describe potential risks and opportunities arising from
memorization in LLMs that we hope will motivate new research directions.
