Tags → #interpretability

Overview

For the first time in the multimodal domain, we demonstrate that features learned by Sparse Autoencoders (SAEs) in a smaller Large Multimodal Model (LMM) can be effectively interpreted by a larger LMM. Our work introduces the use of SAEs to analyze the open-semantic features of LMMs, providing a breakthrough solution for feature interpretation across various model scales.

Inspiration and Motivation

This research is inspired by Anthropic’s remarkable work on applying SAEs to interpret features in large-scale language models. In multimodal models, we discovered intriguing features that:

• Correlate with diverse semantics across visual and textual modalities
• Can be leveraged to steer model behavior for precise control
• Enable deeper understanding of LMM functionality and decision-making

Technical Approach

SAE Training Pipeline

The Sparse Autoencoder (SAE) is trained using a targeted approach:

1. Integration Strategy - SAE integrated into a specific layer of the model
2. Frozen Architecture - All other model components remain frozen during training
3. Training Data - Utilizes LLaVA-NeXT dataset for comprehensive multimodal coverage
4. Feature Learning - Learns sparse, interpretable representations of multimodal features

Auto-Explanation Pipeline

Our novel auto-explanation pipeline analyzes visual features through:

• Activation Region Analysis - Identifies where features activate in visual inputs
• Semantic Correlation - Maps features to interpretable semantic concepts
• Cross-Modal Understanding - Leverages larger LMMs for feature interpretation
• Automated Processing - Scalable interpretation without manual annotation

Feature Steering and Control

Behavioral Control Capabilities

The learned features enable precise model steering by:

• Selective Feature Activation - Amplifying specific semantic features
• Behavioral Modification - Directing model attention and responses
• Interpretable Control - Understanding why specific outputs are generated
• Fine-Grained Manipulation - Precise control over model behavior

Key Contributions

🔬 First Multimodal SAE Implementation

Pioneering application of SAE methodology to multimodal models, opening new research directions in mechanistic interpretability.

🎯 Cross-Scale Feature Interpretation

Demonstration that smaller LMMs can learn features interpretable by larger models, enabling scalable analysis approaches.

🎮 Model Steering Capabilities

Practical application of learned features for controllable model behavior and output generation.

🔄 Auto-Explanation Pipeline

Automated methodology for interpreting visual features without requiring manual semantic labeling.

Research Impact

Mechanistic Interpretability Advancement

This work represents a significant advancement in understanding how multimodal models process and integrate information across modalities.

Practical Applications

• Model Debugging - Understanding failure modes and biases
• Controllable Generation - Steering model outputs for specific applications
• Safety and Alignment - Better control over model behavior
• Feature Analysis - Deep understanding of learned representations

Future Directions

Our methodology opens new research avenues in:

1. Cross-Modal Feature Analysis - Understanding feature interactions across modalities
2. Scalable Interpretability - Extending to larger and more complex models
3. Real-Time Steering - Dynamic control during inference
4. Safety Applications - Preventing harmful or biased outputs

Technical Details

Architecture Integration

The SAE is carefully integrated to:

• Preserve Model Performance - Minimal impact on original capabilities
• Capture Rich Features - Learn meaningful sparse representations
• Enable Interpretation - Facilitate analysis by larger models
• Support Steering - Allow runtime behavioral modification

Evaluation Methodology

Our approach is validated through:

• Feature Interpretability - Qualitative analysis of learned features
• Steering Effectiveness - Quantitative measurement of behavioral control
• Cross-Model Validation - Testing interpretation across different model sizes
• Semantic Consistency - Verifying feature stability and meaning

Conclusion

Multimodal-SAE represents a breakthrough in multimodal mechanistic interpretability, providing the first successful demonstration of SAE-based feature interpretation in the multimodal domain. Our work enables:

• Deeper Understanding of how LMMs process multimodal information
• Practical Control over model behavior through feature steering
• Scalable Interpretation methods for increasingly complex models
• Foundation Research for future advances in multimodal AI safety and control

This research establishes a new paradigm for understanding and controlling Large Multimodal Models, with significant implications for AI safety, controllability, and interpretability research.