Multimodal Model Diffing for Feature Discovery and Control
Researchers introduce MMDiff, a framework using sparse autoencoders to identify and control specific internal features within multimodal large language models.
- Introduces MMDiff, a framework for multimodal model-diffing.
- Uses sparse autoencoders (SAEs) to make multimodal hidden states interpretable.
- Enables targeted control over specific model features rather than just post-hoc inspection.
- Solves the difficulty of isolating features changed specifically by multimodal training.
Current multimodal large language models (MLLMs) demonstrate impressive visual reasoning, but the internal mechanisms driving these capabilities are largely opaque. While sparse autoencoders (SAEs) have been used to interpret text-based models, applying them to multimodal architectures has proven difficult because multimodal training complicates feature isolation.
MMDiff addresses this by providing a framework to train multimodal SAEs that can distinguish between features triggered by text versus those triggered by visual inputs. This allows researchers to move beyond simple observation toward active, targeted control of model behaviors.
By turning hidden states into interpretable interfaces, this method provides a pathway for auditing how models process visual information and ensuring that specific visual concepts are correctly mapped within the model's latent space.
Provides new tools for debugging and controlling multimodal model behaviors at the feature level.
Improves the transparency and safety of AI models that see and hear.
- Sparse Autoencoders (SAEs)
- Neural networks used to decompose complex, dense activations into a set of interpretable, sparse features.
- Multimodal Large Language Models (MLLMs)
- AI models capable of processing and reasoning across different types of data, such as text and images.
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