Style Vectors for Steering Generative Large Language Model
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.
Code (1)
Tasks
Language ModelingLanguage ModellingLarge Language ModelPrompt EngineeringText GenerationSimilar Papers 제목 키워드 기반
LouvreSAE: Sparse Autoencoders for Interpretable and Controllable Style Transfer
Artistic style transfer in generative models remains a significant challenge, as existing methods often introduce style only via model fine-tuning, additional adapters, or prompt engineering, all of which can be computat…
Prompt EngineeringStyle TransferGenerative Modeling of Individual Behavior at Scale
There has been a growing interest in using AI to model human behavior, particularly in domains where humans interact with this technology. While most existing work models human behavior at an aggregate level, our goal is…
Image GenerationMulti-Task Learningparameter-efficient fine-tuningOn the Limits of Steering Vectors for Preference-Aligned Generation
Steering vectors have emerged as a promising approach to controlled text generation, offering interpretable, training-free mechanisms for shaping model outputs. However, their practical generality remains poorly understo…
Text GenerationCan sparse autoencoders be used to decompose and interpret steering vectors?
Steering vectors are a promising approach to control the behaviour of large language models. However, their underlying mechanisms remain poorly understood. While sparse autoencoders (SAEs) may offer a potential method to…
Steering Risk Preferences in Large Language Models by Aligning Behavioral and Neural Representations
Changing the behavior of large language models (LLMs) can be as straightforward as editing the Transformer's residual streams using appropriately constructed "steering vectors." These modifications to internal neural act…