Representation Engineering is the practice of reading the AI's "thoughts" and steering its behavior by detecting and modifying high-level concepts inside the model as it generates responses.
Our approach allows us to identify and manipulate internal model representations without requiring model retraining. It operates on the principle that neural networks develop rich internal representations of concepts, and these can be detected and modified to achieve desired behaviors.
The process of detecting and interpreting representations within model activations.
The process of modifying activations to influence model behavior in real-time.
Create contrastive pairs that clearly distinguish between desired and undesired behaviors.
Extract activations from specific model layers for both positive and negative examples.
Use methods like PCA, LDA, or simple difference to learn control directions. Train classifiers to better understand the interal layer information.
Use learned representations for detection tokens that are harmful and hallucinated and steer the model in a better direction to produce higher quality tokens during inference.
Detect potentially harmful or malicious code patterns in generated responses.
Identify and mitigate various forms of bias in model outputs.
Specifically detect and address gender-based biases in responses.
Identify when models generate false or fabricated information.
Monitor and block harmful, toxic, or dangerous content generation.
Detect and prevent leakage of personal or sensitive information.
Representation Engineering builds on decades of research in interpretability, neuroscience, and machine learning:
Works during inference without requiring model retraining.
Target specific behaviors while preserving model capabilities.
Provides insights into model decisions and learned concepts.
Can be applied to models of different sizes and architectures.