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.

本研究通过在文本生成过程中向隐藏层的激活添加风格向量，探索将大型语言模型 (LLMs) 的输出引导到特定风格 (如情感、情绪或写作风格) 的策略。通过一系列实验，我们展示了使用这种风格向量进行激活工程对生成文本的风格产生影响的有效性和可调节性，使其与提示工程相区别，从而促进了更具适应性和有效性的 AI 增强交互系统的发展。