  As large language models (LLMs) have gained popularity for a variety of use
cases, making them adaptable and controllable has become increasingly
important, especially for user-facing applications. While the existing
literature on LLM adaptation primarily focuses on finding a model (or models)
that optimizes a single predefined objective, here we focus on the challenging
case where the model must dynamically adapt to diverse -- and often changing --
user preferences. For this, we leverage adaptation methods based on linear
weight interpolation, casting them as continuous multi-domain interpolators
that produce models with specific prescribed generation characteristics
on-the-fly. Specifically, we use low-rank updates to fine-tune a base model to
various different domains, yielding a set of anchor models with distinct
generation profiles. Then, we use the weight updates of these anchor models to
parametrize the entire (infinite) class of models contained within their convex
hull. We empirically show that varying the interpolation weights yields
predictable and consistent change in the model outputs with respect to all of
the controlled attributes. We find that there is little entanglement between
most attributes and identify and discuss the pairs of attributes for which this
is not the case. Our results suggest that linearly interpolating between the
weights of fine-tuned models facilitates predictable, fine-grained control of
model outputs with respect to multiple stylistic characteristics
simultaneously.
