Style Vectors for Steering Generative Large Language Model
Kai Konen Sophie Jentzsch Diaoulé Diallo Peer Schütt Oliver Bensch Roxanne El Baff Dominik Opitz Tobias Hecking
This research explores strategies for steering the output of large language
models (LLMs) towards specific styles, such as sentiment, emotion, or writing
style, by adding style vectors to the activations of hidden layers during text
generation. We show that style vectors can be simply computed from recorded
layer activations for input texts in a specific style in contrast to more
complex training-based approaches. Through a series of experiments, we
demonstrate the effectiveness of activation engineering using such style
vectors to influence the style of generated text in a nuanced and
parameterisable way, distinguishing it from prompt engineering. The presented
research constitutes a significant step towards developing more adaptive and
effective AI-empowered interactive systems.