Representation Engineering
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.
Representation Reading
The process of detecting and interpreting representations within model activations.
Identifying harmful intent patternsDetecting bias representationsRecognizing truthfulness indicatorsFinding style and personality markersRepresentation Steering
The process of modifying activations to influence model behavior in real-time.
Steering away from harmful outputsPromoting truthful responsesReducing bias in generationsAdjusting style and toneThe Representation Engineering Pipeline
Data Collection
Create contrastive pairs that clearly distinguish between desired and undesired behaviors.
Activation Extraction
Extract activations from specific model layers for both positive and negative examples.
Pattern Learning
Use methods like PCA, LDA, or simple difference to learn control directions. Train classifiers to better understand the interal layer information.
Deployment
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.
Applications
Bad Code Detection
Last update: 2 weeks ago
Detect potentially harmful or malicious code patterns in generated responses.
Bias Detection
Last update: 2 weeks ago
Identify and mitigate various forms of bias in model outputs.
Gender Bias Detection
Last update: 2 weeks ago
Specifically detect and address gender-based biases in responses.
Hallucination Detection
Last update: 2 weeks ago
Identify when models generate false or fabricated information.
Harmful Content Detection
Last update: 2 weeks ago
Monitor and block harmful, toxic, or dangerous content generation.
Personal Info Detection
Last update: 2 weeks ago
Detect and prevent leakage of personal sensitive information.
Research Foundation
Representation Engineering builds on decades of research in interpretability, neuroscience, and machine learning:
Key Research Areas
Neural network interpretabilityActivation analysis and probingConcept bottleneck modelsLatent space manipulationKey Research Areas
Linear probing and concept detectionActivation patching and causal analysisControl vectors and steering methodsMechanistic interpretabilityAdvantages of Representation Engineering
Real-time Operation
Works during inference without requiring model retraining.
Precision Control
Target specific behaviors while preserving model capabilities.
Interpretable
Provides insights into model decisions and learned concepts.
Scalable
Can be applied to models of different sizes and architectures.
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