Summary of Words in Motion: Extracting Interpretable Control Vectors For Motion Transformers, by Omer Sahin Tas and Royden Wagner
Words in Motion: Extracting Interpretable Control Vectors for Motion Transformers
by Omer Sahin Tas, Royden Wagner
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a method to interpret and control the hidden states of transformer-based models, specifically for motion forecasting. Linear probes are used to measure neural collapse towards interpretable motion features in hidden states. The authors show that high probing accuracy implies meaningful directions and distances between hidden states of opposing features, which can be used to fit interpretable control vectors for activation steering at inference. To optimize the control vectors, sparse autoencoders with fully-connected, convolutional, and MLPMixer layers are used. The approach enables mechanistic interpretability and zero-shot generalization to unseen dataset characteristics with negligible computational overhead. This work is particularly relevant in motion forecasting applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us understand how machines can make predictions about future movements. It’s like trying to figure out what someone will do next based on past actions. The authors create a way to “see” inside the machine’s brain and make it produce more understandable results. They use special tools to help the machine understand what it’s doing, which makes its predictions better. This is important because it can be used in real-life situations like traffic forecasting or autonomous vehicles. |
Keywords
* Artificial intelligence * Generalization * Inference * Transformer * Zero shot
