Mechanistically Interpreting Compression in Vision-Language Models

1. 1Apply causal circuit analysis to identify changes in model internals after compression
2. 2Use crosscoder-based feature comparisons to examine the effects of pruning and quantization on model representations
3. 3Analyze the results to understand how compression affects internal computations and safety behaviors
4. 4Use the insights gained to inform decisions on model deployment and optimization

AI engineers and researchers working on vision-language models can benefit from this study to understand the impact of compression on model internals and safety behaviors. This knowledge can inform decisions on model deployment and optimization

💡 Compression can fundamentally change the internals of vision-language models, affecting internal computations and safety behaviors

💡 Understanding compression in vision-language models: causal circuit analysis reveals changes in model internals #AI #VLMs