  In-context learning (ICL) is a recent advancement in the capabilities of
large language models (LLMs). This feature allows users to perform a new task
without updating the model. Concretely, users can address tasks during the
inference time by conditioning on a few input-label pair demonstrations along
with the test input. It is different than the conventional fine-tuning paradigm
and offers more flexibility. However, this capability also introduces potential
issues. For example, users may use the model on any data without restriction,
such as performing tasks with improper or sensitive content, which might
violate the model policy or conflict with the model owner's interests. As a
model owner, it is crucial to establish a mechanism to control the model's
behavior under ICL, depending on the model owner's requirements for various
content. To this end, we introduce the concept of "applicability authorization"
tailored for LLMs, particularly for ICL behavior, and propose a simple
approach, ICLGuard. It is a fine-tuning framework designed to allow the model
owner to regulate ICL behavior on different data. ICLGuard preserves the
original LLM and fine-tunes only a minimal set of additional trainable
parameters to "guard" the LLM. Empirical results show that the guarded LLM can
deactivate its ICL ability on target data without affecting its ICL ability on
other data and its general functionality across all data.
